Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimodal databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary modalities. We perform retrieval in a two-stage fashion: first rank by a secondary modality, and then perform CBIR only on the top-K items. Thus, effectiveness is improved by performing CBIR on a ‘better’ subset. Using a relatively ‘cheap’ first stage, efficiency is also improved via the fewer CBIR operations performed. Our main novelty is that K is dynamic, i.e. estimated per query to optimize a predefined effectiveness measure. We show that such dynamic two-stage setups can be significantly more effective and robust than similar setups with static thresholds previously proposed.
This document describes MMRetrieval.net, a multimodal search engine that allows retrieval of information from multiple modalities including text and images. It discusses challenges in multimodal retrieval like defining modality, fusing scores across modalities, and improving efficiency through two-stage retrieval. MMRetrieval.net was developed by researchers at DUTH to participate in the ImageCLEF 2010 Wikipedia retrieval task, where it achieved the second best performance overall by combining text and visual features through various fusion techniques. The system demonstrates the promise of multimodal search but also identifies open challenges around modality definitions, fusion methods, and scaling to large databases.
ICFHR 2014 Competition on Handwritten KeyWord Spotting (H-KWS 2014)Konstantinos Zagoris
H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation- free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation- based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.
A system was developed able to retrieve specific documents from a document collection. In this system the query is given in text by the user and then transformed into image. Appropriate features were in order to capture the general shape of the query, and ignore details due to noise or different fonts. In order to demonstrate the effectiveness of our system, we used a collection of noisy documents and we compared our results with those of a commercial OCR package.
The document proposes a new technique for automatic image annotation and retrieval using the Joint Composite Descriptor (JCD). It utilizes two sets of keywords - colors and words - to allow users to naturally specify queries. The JCD fusion combines the Color and Edge Directivity Descriptor (CEDD) and Fuzzy Color and Texture Histogram (FCTH) to compactly represent images. Color and Portrait Similarity Grades are calculated from the JCD to measure keyword similarity. Support Vector Machines further determine portrait similarity. The technique was implemented in an image retrieval web application and evaluated on databases, demonstrating effectiveness in retrieving results consistent with human perception.
Textual information in images constitutes a very rich source of high-level semantics for retrieval and indexing. In this paper, a new approach is proposed using Cellular Automata (CA) which strives towards identifying scene text on natural images. Initially, a binary edge map is calculated. Then, taking advantage of the CA flexibility, the transition rules are changing and are applied in four consecutive steps resulting in four time steps CA evolution. Finally, a post-processing technique based on edge projection analysis is employed for high density edge images concerning the elimination of possible false positives. Evaluation results indicate considerable performance gains without sacrificing text detection accuracy.
Color reduction using the combination of the kohonen self organized feature m...Konstantinos Zagoris
The color of the digital images is one of the most important components of the image processing research area. In many applications such as image segmentation, analysis, compression and transition, it is preferable to reduce the colors as much as possible. In this paper, a color clustering technique which is the combination of a neural network and a fuzzy algorithm is proposed. Initially, the Kohonen Self Organized Featured Map (KSOFM) is applied to the original image. Then, the KSOFM results are fed to the Gustafson-Kessel (GK) fuzzy clustering algorithm as starting values. Finally, the output classes of GK algorithm define the numbers of colors of which the image will be reduced.
This document proposes a method for annotating faces in images without supervision by mining the web. The method has two steps:
1. It ranks faces retrieved from a text-based search engine based on a local density score, which measures how similar a face is to its neighbors. Faces with higher scores are considered more relevant.
2. It then improves this ranking by modeling it as a classification problem, where faces are classified as the queried person or not. Multiple weak classifiers are trained on different subsets and combined via bagging to reduce noise from the unlabeled data. The faces are then re-ranked based on the classifier probabilities. Repeating this process iteratively improves the ranking.
IRJET - Object Detection using Deep Learning with OpenCV and PythonIRJET Journal
This document summarizes research on object detection techniques using deep learning. It discusses using the YOLO algorithm to identify objects in images using a single neural network that predicts bounding boxes and class probabilities. The document reviews prior research on algorithms like R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. It then describes the YOLO loss function and methodology for finding bounding boxes of objects in an image. The document concludes that YOLO is well-suited for real-time object detection applications due to its advantages over other algorithms.
This document describes MMRetrieval.net, a multimodal search engine that allows retrieval of information from multiple modalities including text and images. It discusses challenges in multimodal retrieval like defining modality, fusing scores across modalities, and improving efficiency through two-stage retrieval. MMRetrieval.net was developed by researchers at DUTH to participate in the ImageCLEF 2010 Wikipedia retrieval task, where it achieved the second best performance overall by combining text and visual features through various fusion techniques. The system demonstrates the promise of multimodal search but also identifies open challenges around modality definitions, fusion methods, and scaling to large databases.
ICFHR 2014 Competition on Handwritten KeyWord Spotting (H-KWS 2014)Konstantinos Zagoris
H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation- free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation- based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.
A system was developed able to retrieve specific documents from a document collection. In this system the query is given in text by the user and then transformed into image. Appropriate features were in order to capture the general shape of the query, and ignore details due to noise or different fonts. In order to demonstrate the effectiveness of our system, we used a collection of noisy documents and we compared our results with those of a commercial OCR package.
The document proposes a new technique for automatic image annotation and retrieval using the Joint Composite Descriptor (JCD). It utilizes two sets of keywords - colors and words - to allow users to naturally specify queries. The JCD fusion combines the Color and Edge Directivity Descriptor (CEDD) and Fuzzy Color and Texture Histogram (FCTH) to compactly represent images. Color and Portrait Similarity Grades are calculated from the JCD to measure keyword similarity. Support Vector Machines further determine portrait similarity. The technique was implemented in an image retrieval web application and evaluated on databases, demonstrating effectiveness in retrieving results consistent with human perception.
Textual information in images constitutes a very rich source of high-level semantics for retrieval and indexing. In this paper, a new approach is proposed using Cellular Automata (CA) which strives towards identifying scene text on natural images. Initially, a binary edge map is calculated. Then, taking advantage of the CA flexibility, the transition rules are changing and are applied in four consecutive steps resulting in four time steps CA evolution. Finally, a post-processing technique based on edge projection analysis is employed for high density edge images concerning the elimination of possible false positives. Evaluation results indicate considerable performance gains without sacrificing text detection accuracy.
Color reduction using the combination of the kohonen self organized feature m...Konstantinos Zagoris
The color of the digital images is one of the most important components of the image processing research area. In many applications such as image segmentation, analysis, compression and transition, it is preferable to reduce the colors as much as possible. In this paper, a color clustering technique which is the combination of a neural network and a fuzzy algorithm is proposed. Initially, the Kohonen Self Organized Featured Map (KSOFM) is applied to the original image. Then, the KSOFM results are fed to the Gustafson-Kessel (GK) fuzzy clustering algorithm as starting values. Finally, the output classes of GK algorithm define the numbers of colors of which the image will be reduced.
This document proposes a method for annotating faces in images without supervision by mining the web. The method has two steps:
1. It ranks faces retrieved from a text-based search engine based on a local density score, which measures how similar a face is to its neighbors. Faces with higher scores are considered more relevant.
2. It then improves this ranking by modeling it as a classification problem, where faces are classified as the queried person or not. Multiple weak classifiers are trained on different subsets and combined via bagging to reduce noise from the unlabeled data. The faces are then re-ranked based on the classifier probabilities. Repeating this process iteratively improves the ranking.
IRJET - Object Detection using Deep Learning with OpenCV and PythonIRJET Journal
This document summarizes research on object detection techniques using deep learning. It discusses using the YOLO algorithm to identify objects in images using a single neural network that predicts bounding boxes and class probabilities. The document reviews prior research on algorithms like R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. It then describes the YOLO loss function and methodology for finding bounding boxes of objects in an image. The document concludes that YOLO is well-suited for real-time object detection applications due to its advantages over other algorithms.
An adaptive-model-for-blind-image-restoration-using-bayesian-approachCemal Ardil
This document describes an adaptive model for blind image restoration using a Bayesian approach. It discusses image degradation and restoration techniques such as inverse filtering, Kalman filtering, and regularization. It then introduces Bayesian inference for image processing, describing how the posterior distribution of an image can be estimated given observed noisy data. The paper formulates the image restoration problem using a Bayesian model, where the observed image is considered the sum of the true image and noise. It describes estimating the true image distribution given the observed data by calculating the relative probabilities of possible true images. The goal is to find the most suitable estimate of the true image to enhance a degraded observed image and reduce blur and noise.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence MatrixCSCJournals
This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance, Euclidean distance and Normalized Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
The document summarizes a master's thesis that proposed improvements to content-based image retrieval systems through clustering and relevance feedback. It describes extracting image features, measuring similarity, clustering images using k-means, obtaining user feedback to reformulate queries, and using support vector machines for relevance feedback. The implementation clustered a database of 9918 images, showed improved search times with clustering, and increased accuracy through iterative relevance feedback. The thesis demonstrated a complete content-based image retrieval system with enhanced performance.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVMFerhat Ozgur Catak
The document describes a proposed approach for robust ensemble classifier combination based on noise removal with one-class SVM. The approach partitions an input dataset into sub-datasets, applies noise removal to each sub-dataset using one-class SVM, creates local classifier ensembles for each sub-dataset, and combines the ensemble classifiers using weighted voting. It aims to improve classification accuracy by reducing noise and training ensemble classifiers on partitions of the data. The document outlines the basic idea, discusses preliminaries like one-class SVM and AdaBoost, and describes experiments to evaluate the proposed approach.
Artem Baklanov - Votes Aggregation Techniques in Geo-Wiki Crowdsourcing Game:...AIST
The document describes techniques used to improve the quality of crowdsourced data from the GEO-Wiki project. It discusses preprocessing steps like blur detection, duplicate detection, and vote aggregation algorithms. Blur detection removed 2% of low-quality images, while duplicate detection based on perceptual hashing removed 6% of redundant votes. Benchmarking algorithms on expert-annotated data showed that majority voting performed comparably to more complex algorithms when there were many accurate volunteers and few spammers. Preprocessing improved results by reducing workload and increasing statistical significance.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
This chapter introduces the theoretical foundations of knowledge mining and intelligent agents. It discusses key concepts like knowledge, intelligent agents, and the fundamental tasks of knowledge discovery in databases. The chapter also provides an overview of several well-developed intelligent agent methodologies, including ant colony optimization, particle swarm optimization, and evolutionary algorithms that can be used for knowledge mining.
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses preprocessing steps like denoising and simplifying the point cloud as well as potential techniques for primitive-based reconstruction from the segmented point cloud.
This is the presentation for the paper "Fractional Step Discriminant Pruning: A Filter Pruning Framework for Deep Convolutional Neural Networks", delivered by N. Gkalelis and V. Mezaris at the 7th IEEE Int. Workshop on Mobile Multimedia Computing (MMC2020) that was held as part of the IEEE Int. Conf. on Multimedia and Expo (ICME), in July 2020.
Feature Subset Selection for High Dimensional Data Using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes the FAST algorithm which has three steps: 1) remove irrelevant features, 2) divide features into clusters using DBSCAN, and 3) select the most representative feature from each cluster. DBSCAN is a density-based clustering algorithm that can identify clusters of varying densities and detect outliers. The FAST algorithm is evaluated to select a small number of discriminative features from high dimensional data in an efficient manner. It aims to remove irrelevant and redundant features to improve predictive accuracy while handling large feature sets.
The document describes tools for image retrieval in large multimedia databases using the Hierarchical Cellular Tree (HCT) indexing technique. It discusses modifications made to the original HCT, including using an approximation for covering radius. Experimental results on a 216,317 image database show the HCT can be built efficiently and retrievals performed in under a second using the Preemptive Cell Search technique, achieving high recall and retrieval rates. Tools implementing HCT indexing and querying were developed along with a server/client architecture.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Radial Thickness Calculation and Visualization for Volumetric Layers-8397Kitware Kitware
The document proposes calculating and visualizing radial thickness for volumetric layers. It defines radial thickness as the distance between corresponding points on two surfaces with the same polar coordinates. An algorithm is described that uses ray tracing to calculate the intersection between rays emitted from a master surface and a supplementary surface to determine radial thickness values at corresponding points. Results are presented visualizing calculated radial thickness values on human skull vault surfaces to demonstrate the method represents layer thickness reasonably.
Comparative Performance Evaluation of Image Descriptors Over IEEE 802.11b Noi...Konstantinos Zagoris
We evaluate the image retrieval
procedure over an IEEE 802.11b Ad Hoc network, operating
in 2.4GHz, using IEEE Distributed Coordination Function
CSMA/CA as the multiple access scheme. IEEE 802.11 is a
widely used network standard, implemented and supported
by a variety of devices, such as desktops, laptops, notebooks,
mobile phones etc., capable of providing a variety of different
services, such as file transfer, internet access et.al. Therefore,
we consider IEEE 802.11b being a suitable technology to
investigate the case of conducting image retrieval over a
wireless noisy channel. The model we use to simulate the noisy
environment is based on the scenario in which the wireless
network is located in an outdoor noisy environment, or in
an indoor environment of partial LOS - Line-of-sight power.
We used a large number of descriptors reported in literature
in order to evaluate which one has the best performance in
terms of Mean Average Precision - MAP values under those
circumstances. Experimental results on known benchmarking
database show that the majority of the descriptors appear to
have decreased performance when transferred and used in such
noisy environments.
Content and Metadata Based Image Document Retrieval (in Greek)Konstantinos Zagoris
In the bottom line, the present thesis presents solutions to real problems of the content-based image retrieval systems as image segmentation, text localization, relevance feedback algorithms and shape/word descriptors. All the proposed methods can be combined in order to create a fast and modern MPEG-7 compatible content-based retrieval image system.
An adaptive-model-for-blind-image-restoration-using-bayesian-approachCemal Ardil
This document describes an adaptive model for blind image restoration using a Bayesian approach. It discusses image degradation and restoration techniques such as inverse filtering, Kalman filtering, and regularization. It then introduces Bayesian inference for image processing, describing how the posterior distribution of an image can be estimated given observed noisy data. The paper formulates the image restoration problem using a Bayesian model, where the observed image is considered the sum of the true image and noise. It describes estimating the true image distribution given the observed data by calculating the relative probabilities of possible true images. The goal is to find the most suitable estimate of the true image to enhance a degraded observed image and reduce blur and noise.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence MatrixCSCJournals
This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance, Euclidean distance and Normalized Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
The document summarizes a master's thesis that proposed improvements to content-based image retrieval systems through clustering and relevance feedback. It describes extracting image features, measuring similarity, clustering images using k-means, obtaining user feedback to reformulate queries, and using support vector machines for relevance feedback. The implementation clustered a database of 9918 images, showed improved search times with clustering, and increased accuracy through iterative relevance feedback. The thesis demonstrated a complete content-based image retrieval system with enhanced performance.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVMFerhat Ozgur Catak
The document describes a proposed approach for robust ensemble classifier combination based on noise removal with one-class SVM. The approach partitions an input dataset into sub-datasets, applies noise removal to each sub-dataset using one-class SVM, creates local classifier ensembles for each sub-dataset, and combines the ensemble classifiers using weighted voting. It aims to improve classification accuracy by reducing noise and training ensemble classifiers on partitions of the data. The document outlines the basic idea, discusses preliminaries like one-class SVM and AdaBoost, and describes experiments to evaluate the proposed approach.
Artem Baklanov - Votes Aggregation Techniques in Geo-Wiki Crowdsourcing Game:...AIST
The document describes techniques used to improve the quality of crowdsourced data from the GEO-Wiki project. It discusses preprocessing steps like blur detection, duplicate detection, and vote aggregation algorithms. Blur detection removed 2% of low-quality images, while duplicate detection based on perceptual hashing removed 6% of redundant votes. Benchmarking algorithms on expert-annotated data showed that majority voting performed comparably to more complex algorithms when there were many accurate volunteers and few spammers. Preprocessing improved results by reducing workload and increasing statistical significance.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
This chapter introduces the theoretical foundations of knowledge mining and intelligent agents. It discusses key concepts like knowledge, intelligent agents, and the fundamental tasks of knowledge discovery in databases. The chapter also provides an overview of several well-developed intelligent agent methodologies, including ant colony optimization, particle swarm optimization, and evolutionary algorithms that can be used for knowledge mining.
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses preprocessing steps like denoising and simplifying the point cloud as well as potential techniques for primitive-based reconstruction from the segmented point cloud.
This is the presentation for the paper "Fractional Step Discriminant Pruning: A Filter Pruning Framework for Deep Convolutional Neural Networks", delivered by N. Gkalelis and V. Mezaris at the 7th IEEE Int. Workshop on Mobile Multimedia Computing (MMC2020) that was held as part of the IEEE Int. Conf. on Multimedia and Expo (ICME), in July 2020.
Feature Subset Selection for High Dimensional Data Using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes the FAST algorithm which has three steps: 1) remove irrelevant features, 2) divide features into clusters using DBSCAN, and 3) select the most representative feature from each cluster. DBSCAN is a density-based clustering algorithm that can identify clusters of varying densities and detect outliers. The FAST algorithm is evaluated to select a small number of discriminative features from high dimensional data in an efficient manner. It aims to remove irrelevant and redundant features to improve predictive accuracy while handling large feature sets.
The document describes tools for image retrieval in large multimedia databases using the Hierarchical Cellular Tree (HCT) indexing technique. It discusses modifications made to the original HCT, including using an approximation for covering radius. Experimental results on a 216,317 image database show the HCT can be built efficiently and retrievals performed in under a second using the Preemptive Cell Search technique, achieving high recall and retrieval rates. Tools implementing HCT indexing and querying were developed along with a server/client architecture.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Radial Thickness Calculation and Visualization for Volumetric Layers-8397Kitware Kitware
The document proposes calculating and visualizing radial thickness for volumetric layers. It defines radial thickness as the distance between corresponding points on two surfaces with the same polar coordinates. An algorithm is described that uses ray tracing to calculate the intersection between rays emitted from a master surface and a supplementary surface to determine radial thickness values at corresponding points. Results are presented visualizing calculated radial thickness values on human skull vault surfaces to demonstrate the method represents layer thickness reasonably.
Comparative Performance Evaluation of Image Descriptors Over IEEE 802.11b Noi...Konstantinos Zagoris
We evaluate the image retrieval
procedure over an IEEE 802.11b Ad Hoc network, operating
in 2.4GHz, using IEEE Distributed Coordination Function
CSMA/CA as the multiple access scheme. IEEE 802.11 is a
widely used network standard, implemented and supported
by a variety of devices, such as desktops, laptops, notebooks,
mobile phones etc., capable of providing a variety of different
services, such as file transfer, internet access et.al. Therefore,
we consider IEEE 802.11b being a suitable technology to
investigate the case of conducting image retrieval over a
wireless noisy channel. The model we use to simulate the noisy
environment is based on the scenario in which the wireless
network is located in an outdoor noisy environment, or in
an indoor environment of partial LOS - Line-of-sight power.
We used a large number of descriptors reported in literature
in order to evaluate which one has the best performance in
terms of Mean Average Precision - MAP values under those
circumstances. Experimental results on known benchmarking
database show that the majority of the descriptors appear to
have decreased performance when transferred and used in such
noisy environments.
Content and Metadata Based Image Document Retrieval (in Greek)Konstantinos Zagoris
In the bottom line, the present thesis presents solutions to real problems of the content-based image retrieval systems as image segmentation, text localization, relevance feedback algorithms and shape/word descriptors. All the proposed methods can be combined in order to create a fast and modern MPEG-7 compatible content-based retrieval image system.
Handwritten and Machine Printed Text Separation in Document Images using the ...Konstantinos Zagoris
In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten,Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.
Mammography is currently the dominant imaging modality for the early detection of breast cancer. However, its robustness in distinguishing malignancy is relatively low, resulting in a large number of unnecessary biopsies. A computer-aided diagnosis (CAD) scheme, capable of visually justifying its results, is expected to aid the decision made by radiologists. Content-based image retrieval (CBIR) accounts for a promising paradigm in this direction. Facing this challenge, we introduce a CBIR scheme that utilizes the extracted features as input to a support vector machine (SVM) ensemble. The final features used for CBIR comprise the participation value of each SVM. The retrieval performance of the proposed scheme has been evaluated quantitatively on the basis of the standard measures. In the experiments, a set of 90 mammograms is used, derived from a widely adopted digital database for screening mammography. The experimental results show the improved performance of the proposed scheme.
Text extraction using document structure features and support vector machinesKonstantinos Zagoris
This document presents a bottom-up text localization technique that uses document structure features and support vector machines. The technique detects and extracts text from document images through several steps: preprocessing, locating and merging blocks, extracting features from blocks, and using support vector machines trained on the features to classify blocks as containing text or not. The technique uses a flexible feature descriptor based on structural elements that can adapt to different document image types. Experimental results on document images show a 98.5% success rate in classifying blocks.
This document discusses an image search system called TsoKaDo that aims to reduce the semantic gap between a user's search query and the images they want to find. TsoKaDo uses the Color and Edge Directivity Descriptor (CEDD) to extract color and texture features from images. It then classifies images using Self Growing and Self Organized Neural Gas Network (SGONG) and k-means clustering. Potential tags are identified using semantic similarity and WordNet. TsoKaDo seeks to improve image search by providing tags that better match images based on their visual content.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
This document discusses content-based image retrieval (CBIR) using interactive genetic algorithms. It proposes using IGA to better capture a user's image preferences through iterative refinement of feature weights. Features discussed include color (mean and standard deviation in HSV color space), texture (entropy based on gray level co-occurrence matrix), and edges. IGA allows users to provide feedback on retrieval results to gradually shape the algorithm toward their interests over multiple generations. The document reviews related work using other features for CBIR and discusses color, texture, and edge features in more detail.
Segmentation - based Historical Handwritten Word Spotting using document-spec...Konstantinos Zagoris
Many word spotting strategies for the modern documents are not directly applicable to historical handwritten documents due to writing styles variety and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that relies upon document-specific local features which take into account texture information around representative keypoints. Experimental work on two historical handwritten datasets using standard evaluation measures shows the improved performance achieved by the proposed methodology.
Content-Based Image Retrieval (CBIR) systems employ colour as primary feature with texture and shape as secondary features. In this project a simple, image retrieval system will be implemented
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
Content based image retrieval using clustering Algorithm(CBIR)Raja Sekar
The document discusses content-based image retrieval (CBIR). It defines CBIR as retrieving images from a collection based on automatically extracted features like color, texture, and shape. The document outlines the history and motivation for CBIR. It discusses features used for retrieval like color, texture, shape. Filtering algorithms and clustering methods used for CBIR are also summarized. Applications of CBIR include medical imaging, stock photography, and military intelligence. CBIR is presented as an effective alternative to text-based image retrieval.
This document summarizes a seminar presentation on Content Based Image Retrieval (CBIR). CBIR allows users to search for digital images in large databases based on the images' visual contents like color, shape, and texture, rather than keywords. The seminar covers the inspiration for CBIR, different types of image retrieval, how CBIR works by extracting features from images, applications like crime prevention and biomedicine, advantages like efficient searching, and limitations like accuracy issues. The goal of CBIR research is to develop algorithms that can characterize and understand images like human vision.
- Content-based image retrieval (CBIR) searches for images based on visual features like color, texture, and shape rather than keywords.
- CBIR systems extract features from images to create metadata and use those features to calculate visual similarity between images.
- Relevance feedback allows users to provide feedback on initial search results to help the system recalculate feature weights and improve subsequent results.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
Faro Visual Attention For Implicit Relevance Feedback In A Content Based Imag...Kalle
In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users’ eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and “pyramid” like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.
The document discusses content-based image retrieval (CBIR). It provides a brief history of CBIR, noting it originated in 1992. It describes challenges of CBIR, including the semantic gap between low-level features extracted and high-level human concepts. It also outlines common CBIR techniques like color, shape, and texture analysis. Applications are described as image search and browsing. Limitations include not fully capturing human visual understanding.
Invited talk at USTC and SJTU, discuss recent progress in object re-identification against very large repository, especially the problem of fast key point detection, feature repeatability prediction, aggregation, and object repository indexing and search.
The incremented desideratum of content based image retrieval system can be found in a number of different domains such as Data Mining, Edification, Medical Imaging, Malefaction Aversion, climate, Remote Sensing and Management of Globe Resources. Google's image search and photo album implements such as image search, Google's Picasa project applications in general gregarious networking environment, the hunt for practical, efficacious image search in the web context. Our application provides the color based image retrieval, utilizing features like dominant color. The color features are obtained through wavelet transformation and color histogram and the amalgamation of these features is robust to scaling and translation of objects in an image. The proposed system has established a promising and more expeditious retrieval method on a input image database containing more general purpose color images. The performance has been analysed by estimating with the subsisting systems in the literature. Dr. Aziz Makandar | Mrs. Rashmi Somshekhar | Miss. Nayan Jadav ""Content Based Image Retrieval"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24047.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/24047/content-based-image-retrieval/dr-aziz-makandar
Image Retrieval using Equalized Histogram Image Bins MomentsIDES Editor
CBIR operates on a totally different principle
from keyword indexing. Primitive features characterizing
image content, such as color, texture, and shape are computed
for both stored and query images, and used to identify the
images most closely matching the query. There have been
many approaches to decide and extract the features of images
in the database. Towards this goal we propose a technique by
which the color content of images is automatically extracted to
form a class of meta-data that is easily indexed. The color
indexing algorithm uses the back-projection of binary color
sets to extract color regions from images. This technique use
without histogram of image histogram bins of red, green and
blue color. The feature vector is composed of mean, standard
deviation and variance of 16 histogram bins of each color
space. The new proposed methods are tested on the database
of 600 images and the results are in the form of precision and
recall.
[DLHacks 実装]Perceptual Adversarial Networks for Image-to-Image TransformationDeep Learning JP
The document discusses two papers on image-to-image translation: Pix2Pix and PAN. Pix2Pix uses a conditional GAN with adversarial and per-pixel losses. PAN builds on Pix2Pix by adding a perceptual adversarial loss using the discriminator's features. This additional loss helps the discriminator better detect differences between real and fake images. The document provides examples of tasks like deraining and inpainting that both frameworks can address and compares their results. It concludes by discussing the author's implementation of Pix2Pix and PAN and results showing Pix2Pix generally outperforming PAN, potentially due to issues like the need for per-pixel loss or patch discriminator design.
This document discusses various image steganography techniques. It begins by introducing image steganography and its applications, such as hiding passwords and encrypting private files. It then covers several embedding and extraction methods, including least significant bit substitution, pixel indicator, stego color cycle, triple-A, Max-bit, optimal pixel adjustment procedure, and inverted pattern approaches. For each method, it provides a brief overview of the technique and how it works to hide information in the image in an imperceptible way.
The document outlines research on automatically constructing diverse, high-quality image datasets. It discusses challenges like visual polysemy, limited diversity, and low accuracy in existing automatic methods. The author's proposed solutions discover multiple textual metadata to improve diversity and accuracy by pruning noisy images and metadata. Experimental results demonstrate the approach's ability to distinguish visual senses, classify images, re-rank search results, and improve classification accuracy and diversity compared to other datasets. The document also discusses applications in fine-grained visual categorization.
1) rasdaman is an array database that supports n-dimensional arrays and datacubes through SQL and array analytics.
2) It uses a tile streaming architecture to handle large raster datasets in a scalable way.
3) The datacube data model from the Open Geospatial Consortium provides a standard way to represent multi-dimensional geospatial datasets.
Multimedia Databases: Performance Measure Benchmarking Model (PMBM) Frameworktheijes
Multimedia database consist of media data types such as text, images, sound, and video, which are retrieved by image descriptors such as texture, shape, and color as their primary key. New technologies have numerous challenges, and multimedia database have its share of challenges. Some of these challenges are in Content-based Image Retrieval (CBIR) techniques, such as irregular performance measurements in motion, location and sketches, hence creating gap in multimedia database evaluation techniques. As a result of which, this paper was motivated to close this gap of lack of an effective and precise performance evaluation benchmarking measure. To address the irregular performance measure, the paper developed a performance measure benchmarking model (PMBM) framework using image descriptors in query by image content (QBIC). Moreover, the paper adopted the de Groot’s empirical research cycle methodology by implementing the five stages methodology of: image preparation, query definition, data collection, data evaluation and framework building for the PMBM framework development. Furthermore, the paper conducted several experiments on texture, color and shape image datasets with respect to performance measures of accuracy, recall, precision and F-Measures on CBIR DB2 database. The results of these experiments were used to develop the PMBM framework and were analyzed by a statistical software SPSS version 20. Even more importantly, the paper developed a software evaluation tool in JAVA programming language from the PMBM framework to qualify and quantify its effectiveness to measure performance of the existing CBIR database systems. The results of this evaluation showed that the open source CBIR database FIRE was ranked as High with baseline score value (BSV) of 0.92 (92%) and IMG(Anaktisi) was ranked as Low, with BSV of 0.87 (87%) from BSV of 0.90 (90%) of IBM DB (benchmark system).
Ivan Khomyakov's portfolio summarizes his skills and experience. He has extensive knowledge of programming languages like C++, C#, Python, and technologies including OpenCV, SQL, machine learning, AWS, and Unity 3D. Some of his projects include developing a fast cubemap filter for rendering environments, a real-time locating system for tracking objects, and a dynamic map module for navigation systems. He also has experience with route editing tools, augmented reality applications, medical image segmentation, and machine learning algorithms. His background includes both academic and professional work on computer vision, image processing, statistics, and more.
Lecture given on January 28, 2019 to post-graduate students of the Computer Engineering and Media program, at the School of Journalism and Media, Aristotle University of Thessaloniki.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
Research Inventy : International Journal of Engineering and Science is publis...researchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
Research Inventy: International Journal of Engineering and Scienceresearchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
Content-based image retrieval based on corel dataset using deep learningIAESIJAI
A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.
High Performance Cyberinfrastructure Enabling Data-Driven Science in the Biom...Larry Smarr
11.04.06
Joint Presentation
UCSD School of Medicine Research Council
Larry Smarr, Calit2 & Phil Papadopoulos, SDSC/Calit2
Title: High Performance Cyberinfrastructure Enabling Data-Driven Science in the Biomedical Sciences
The document provides an introduction to computer vision concepts including neural network structures, activation functions, convolution operators, pooling layers, and batch normalization. It then discusses image classification, including popular datasets, classification networks from LeNet to DLA, and experiments on car brand classification. Finally, it covers object detection, comparing region-based methods like R-CNN, Fast R-CNN, Faster R-CNN, and R-FCN to region-free methods like YOLO.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
20240605 QFM017 Machine Intelligence Reading List May 2024
Dynamic Two-Stage Image Retrieval from Large Multimodal Databases
1. Dynamic Two-Stage Image Retrieval from
Large Multimodal Databases
Avi Arampatzis
Konstantinos Zagoris
Savvas Chatzichristofis
Department of Electrical and Computer Engineering
Democritus University of Thrace
Xanthi 67100, Greece
ECIR 2011
2. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 2
Outline
1. Content-based Image Retrieval (CBIR): global vs local features, scalability
2. A problem of global features: The Red Tomato / Red Pie-Chart Problem
3. Multimodal Information Collections
4. Two-stage Image Retrieval from Multimedia Databases
related work
our main contributions
5. Experiments on the ImageCLEF 2010 Wikipedia Test Collection
3. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 3
Content-based Image Retrieval (CBIR)
In CBIR, images are represented by either
local features:
¦ computed at multiple image points; capable of recognizing objects
¦ computationally expensive, e.g. due to high dimensionality
global features:
¦ generalize images with single vectors capturing color, texture, shape, etc.
¦ computationally cheap (relatively)
¥ thus, more popular
CBIR with either global or local features
does not scale up well to large databases efficiency-wise
4. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 4
CBIR with Global Features
notoriously noisy for image queries of low generality
(generality = the fraction of relevant images in a collection)
In text retrieval: documents matching no query keywords are not retrieved
In image retrieval: CBIR will rank the whole collection
¦ The Red Tomato / Red Pie-Chart Problem [next]
5. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 5
The Red Tomato / Red Pie-Chart Problem
Image query: Collection items:
If the query has a low generality
early ranks may be dominated by spurious results (such as the pie-chart)
possibly ranked even before red tomatoes on non-white backgrounds
6.
7. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 7
Contemporary Information Collections
Large
Multimodal (= provide different manners of retrieval )
A good example is Wikipedia:
Large:
3,605,426 articles (English version alone), “millions of images”, etc.
Multimodal:
Topics are covered in several languages.
Topics may include non-textual media (image, sound, video, etc.)
annotated in a variety of metadata fields (caption, description, etc.)
Thus, there are several ways to get to the same info/topic.
8. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 8
Image Retrieval from Multimodal Databases
In image retrieval systems, users are assumed to target visual similarity. Thus,
Image is the primary medium.
image modalities are most important than others
Modalities from other media are secondary.
but they can still help with the problems of CBIR:
¦ low generality (Red Tomato/Pie-Chart) problem (esp. for global feats.)
¦ speed
Traditionally, fusion has been used, but:
weighing of media is not trivial; usually requires training data
not theoretically sound: influence of textual scores may worsen the visual quality
9. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 9
Two-stage Image Retrieval from Multimedia Databases
Key idea for improving CBIR effectiveness:
Before CBIR, raise query generality by reducing collection size via filtering.
Assumptions:
Query is expressed in the primary medium i.e. image,
accompanied by a query in a secondary medium, e.g. text.
Two stage retrieval:
Rank the collection by the secondary medium/query, e.g. text.
Draw a rank threshold K.
Re-rank only the top-K items with CBIR.
Using a ‘cheaper’ secondary medium, we also improve speed.
10. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 10
Previous Related Work
Best-results re-ranking by visual content has been seen before, but
for other purposes, e.g. result clustering, diversity, . . .
using external info (e.g. sets of images), or training data, . . .
They all used
global features
a static predefined threshold for all queries
Effectiveness results have been mixed,
while some didn’t provide a comparative evaluation.
11. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 11
Main Contributions
In view of the related literature, our main contributions are:
dynamic thresholds per query
no external information
no training data
extensive evaluation of thresholding types and levels
12. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 12
The ImageCLEF 2010 Wikipedia Test Collection
237,434 items
Primary medium: image
¦ Heterogeneous: color natural images, graphics, greyscale images, etc.
¦ variety of image sizes
¦ It’s a large benchmark image database for today’s standards.
+ noisy and incomplete user-supplied annotations
+ wikipedia articles containing the images
Annotations articles come in 3 languages: En, Fr, De.
70 test topics
Visual part:
¦ 1 or more example images
Textual part:
¦ 3 titles fields, one per language (En, Fr, De)
Topics are assessed by visual similarity.
13. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 13
Indexing and Retrieval
text: only English annotations + English query
Lemur Toolkit V4.11 / Indri V2.11
¦ tf.idf retrieval model (works better with our thresholding methods)
¦ default settings + Krovetz stemmer
image:
Joint Composite Descriptor (JCD)
¦ captures color and texture information
¦ developed for color natural images
Spatial Color Distribution (SpCD)
¦ captures color and its spatial distribution
¦ more suitable for colored graphics (fewer colors, less texture)
JCD SpCD are found to be more effective than MPEG-7 descriptors.
14. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 14
Thresholding and Re-ranking
Static Thresholding: a fixed pre-selected rank threshold K for all topics
K 25, 50, 100, 250, 500, 1000.
Dynamic Thresholding: a variable rank threshold per topic
Score-Distributional Threshold Optimization (SDTO)
[Arampatzis et.al., ”Where to Stop Reading a Ranked-list?”, SIGIR 2009]
Two types:
¦ Threshold on Precision: g 0.990, 0.950, 0.800, 0.500, 0.330, 0.100
¦ Threshold on prel: θ 0.990, 0.950, 0.800, 0.500, 0.330, 0.100
15. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 15
Initial Experiments: Setting a Baseline
item scoring by MAP P@10 P@20 bpref
JCD1 .0058 .0486 .0479 .0352
maxi JCDi .0072 .0614 .0614 .0387
maxi JCDi + maxi SpCDi .0112 .0871 .0886 .0415
tf.idf (text-only) .1293 .3614 .3314 .1806
Image-only runs provide very weak baselines.
We chose the text-only run as a baseline for the statistical significance testing.
This makes sense also from an efficiency point of view:
if using a secondary text modality for image retrieval is more effective than
current CBIR methods, then there is no reason at all for using
computationally costly CBIR methods.
17. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 17
Results
Static thresholding improves initial Precision at the cost of MAP and bpref.
Dynamic thresholding on Precision or prel does not have this drawback.
The level of static and precision thresholds influences greatly the effectiveness;
unsuitable choices (e.g. too loose) lead to a degraded performance.
Prel thresholds are much more robust in this respect.
Better CBIR at the second stage leads to overall improvements, but the
thresholding type seems more important: While the two CBIR methods vary
greatly in performance (the best has almost double the effectiveness of the
other), static thresholding is not influenced much by this choice. Dynamic
methods benefit more from improved CBIR.
18. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 18
Results
19.
20. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 20
Conclusions
Two-stage retrieval with dynamic thresholding:
more effective robust than static thresholding
practically insensitive to a wide range of choices for the optimization measure
beats significantly the text-only several image-only baselines
Two-stage retrieval, irrespective of thresholding type:
efficiency benefit:
¦ it cuts down greatly on expensive image operations
¦ on average, only 2 to 5 in 10,000 images had to be scored
21. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 21
Further Research
Compare two-stage retrieval to fusion. [Did this; check ECIR11 poster]
Both methods work better than text-only image-only;
two-stage is slightly better than fusion, but the difference is non-significant.
Both methods are robust in different ways:
¦ Fusion provides less variability across topics but it is sensitive to the
weighing parameter of the contributing media.
¦ Two-stage provides a much lower sensitivity to its thresholding parameter
but has a higher variability across topics.
Try two-stage retrieval with other type of image descriptors,
e.g. the visual codebook approach (TOP-SURF).
Generalization to multi-stage retrieval, where rankings for the media are
successively being thresholded and re-ranked with respect to a media hierarchy.
22. Dynamic Two-Stage Image Retrieval from Large Multimodal Databases Avi Arampatzis, Konstantinos Zagoris, Savvas Chatzichristofis 22
avi@ee.duth.gr
Thank you !