CAIR is a multi-scale color attention network for removing filters from Instagram images. It uses a color attention module inspired by previous work to capture color distributions at different scales. The proposed CAIR model has a single-scale and multi-scale version, and also an ensemble model combining both. In experiments on the IFFI dataset, CAIR achieved state-of-the-art results for Instagram filter removal with faster runtimes and fewer parameters than previous methods.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
A Survey on Content Based Image Retrieval SystemYogeshIJTSRD
The increasing increase of picture databases in practically every industry, including medical science, multimedia, geographic information systems, photography, journalism, and so on, necessitates the development of an effective and efficient approach for image processing. The approach of content based image retrieval is used to recover images based on their content, such as texture, colour, shape, and spatial layout. However, because to the semantic mismatch between the users high level notions and the images low level properties, retrieving the image is extremely challenging. Many concepts were presented in effort to close this gap. Furthermore, images can be stored and extracted depending on a variety of properties, one of which being texture. Content based Image Retrieval has become a popular study area as a result of the growth of video and image data in digital form. Digital data, such as criminal photographs, fingerprints, and scene photographs, has been widely used in forensic sciences. As a result, arranging such enormous amounts of visual data, such as how to quickly find an interesting image, becomes a major difficulty. There is a pressing need to develop an effective method for locating photographs. An image must be represented with particular features in order to be found. Three significant visual qualities of an image are colour, texture, and shape. The search for images utilising colour, texture, and shape attributes has gotten a lot of press. Preeti Sondhi | Umar Bashir "A Survey on Content Based Image Retrieval System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43777.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/43777/a-survey-on-content-based-image-retrieval-system/preeti-sondhi
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
A Survey on Content Based Image Retrieval SystemYogeshIJTSRD
The increasing increase of picture databases in practically every industry, including medical science, multimedia, geographic information systems, photography, journalism, and so on, necessitates the development of an effective and efficient approach for image processing. The approach of content based image retrieval is used to recover images based on their content, such as texture, colour, shape, and spatial layout. However, because to the semantic mismatch between the users high level notions and the images low level properties, retrieving the image is extremely challenging. Many concepts were presented in effort to close this gap. Furthermore, images can be stored and extracted depending on a variety of properties, one of which being texture. Content based Image Retrieval has become a popular study area as a result of the growth of video and image data in digital form. Digital data, such as criminal photographs, fingerprints, and scene photographs, has been widely used in forensic sciences. As a result, arranging such enormous amounts of visual data, such as how to quickly find an interesting image, becomes a major difficulty. There is a pressing need to develop an effective method for locating photographs. An image must be represented with particular features in order to be found. Three significant visual qualities of an image are colour, texture, and shape. The search for images utilising colour, texture, and shape attributes has gotten a lot of press. Preeti Sondhi | Umar Bashir "A Survey on Content Based Image Retrieval System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43777.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/43777/a-survey-on-content-based-image-retrieval-system/preeti-sondhi
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This Presentation is used in the dissertation of the Eren Golge's Master of Science. It proposes 2 new procedure to learn visual concept models from noisy image sources without any human annotation.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
Content-based image retrieval (CBIR) uses the content features for
retrieving and searching the images in a given large database. Earlier,
different hand feature descriptor designs are researched based on cues that
are visual such as shape, colour, and texture used to represent these images.
Although, deep learning technologies have widely been applied as an
alternative to designing engineering that is dominant for over a decade. The
features are automatically learnt through the data. This research work
proposes integrated dual deep convolutional neural network (IDD-CNN),
IDD-CNN comprises two distinctive CNN, first CNN exploits the features
and further custom CNN is designed for exploiting the custom features.
Moreover, a novel directed graph is designed that comprises the two blocks
i.e. learning block and memory block which helps in finding the similarity
among images; since this research considers the large dataset, an optimal
strategy is introduced for compact features. Moreover, IDD-CNN is
evaluated considering the two distinctive benchmark datasets the oxford
dataset considering mean average precision (mAP) metrics and comparative
analysis shows IDD-CNN outperforms the other existing model.
Multivariate feature descriptor based cbir model to query large image databasesIJARIIT
The content based image retrieval (CBIR) applications have grown their popularity in the past decade with the
exponential growth in the image data volumes. The social networks have aggravated the size of image data on the internet. Social
network enables everyone to upload the images of one’s choice, which becomes the reason behind aggregation of millions of
images on the cyber space. It’s not possible to query these large image databases with the ordinary methods. Hence there was a
strong requirement of a smart and intelligent method to discover the similar images, which has been accomplished by using the
machine learning methods. In this paper, the multivariate feature descriptor method has been presented to extract the required
and relevant information from the large image databases. The proposed multivariate method involves the image color and texture
for the purpose of image matching to the query image (also known as a reference image). The most matching entities are returned
as the final results by the image extraction method. There are four methods, which involves three singular feature and one
multivariate feature based models, have been implemented. The multivariate model has been found much stable and returned
the maximum accuracy under this model.
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.
Content-based Image Retrieval System for an Image Gallery Search Application IJECEIAES
Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient browsing through three different search modes: retrieval by image query, retrieval by facial recognition, and retrieval by text or tags. In this project, the MPEG-7-like Powered Localized Color and Edge Directivity Descriptor is used to extract the feature vectors of the image database and the facial recognition system is built around the Eigenfaces concept. A graphical user interface with the basic functionality of an image gallery application is also developed to implement the three search modes. Results show that the application is able to retrieve and display images in a collection as thumbnail previews with high retrieval accuracy and medium relevance and the computational requirements for subsequent searches were significantly reduced through the incorporation of text-based image retrieval as one of the search modes. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.
In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.
Adversarial Photo Frame: Concealing Sensitive Scene Information in a User-Acc...multimediaeval
Paper: http://ceur-ws.org/Vol-2670/MediaEval_19_paper_24.pdf
Youtube: https://www.youtube.com/watch?v=keLM9fmKJSI
Zhuoran Liu and Zhengyu Zhao, Adversarial Photo Frame: Concealing Sensitive Scene Information of Social Images in a User-Acceptable Manner. Proc. of MediaEval 2018, 27-29 October 2019, Sophia Antipolis, France.
Abstract:
Personal privacy protection has become more and more crucial in the era of big multimedia data and artificial intelligence. This paper presents our submission to pixel privacy task, where we propose to fool the deep visual classification model that is for recognition of sensitive scenes by adding adversarial frame to the image. Experimental results indicate that our method can achieve strong adversarial effects while maintaining the visual appeal and social function of the transformed images.
Presented by Zhengyu Zhao
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This Presentation is used in the dissertation of the Eren Golge's Master of Science. It proposes 2 new procedure to learn visual concept models from noisy image sources without any human annotation.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
Content-based image retrieval (CBIR) uses the content features for
retrieving and searching the images in a given large database. Earlier,
different hand feature descriptor designs are researched based on cues that
are visual such as shape, colour, and texture used to represent these images.
Although, deep learning technologies have widely been applied as an
alternative to designing engineering that is dominant for over a decade. The
features are automatically learnt through the data. This research work
proposes integrated dual deep convolutional neural network (IDD-CNN),
IDD-CNN comprises two distinctive CNN, first CNN exploits the features
and further custom CNN is designed for exploiting the custom features.
Moreover, a novel directed graph is designed that comprises the two blocks
i.e. learning block and memory block which helps in finding the similarity
among images; since this research considers the large dataset, an optimal
strategy is introduced for compact features. Moreover, IDD-CNN is
evaluated considering the two distinctive benchmark datasets the oxford
dataset considering mean average precision (mAP) metrics and comparative
analysis shows IDD-CNN outperforms the other existing model.
Multivariate feature descriptor based cbir model to query large image databasesIJARIIT
The content based image retrieval (CBIR) applications have grown their popularity in the past decade with the
exponential growth in the image data volumes. The social networks have aggravated the size of image data on the internet. Social
network enables everyone to upload the images of one’s choice, which becomes the reason behind aggregation of millions of
images on the cyber space. It’s not possible to query these large image databases with the ordinary methods. Hence there was a
strong requirement of a smart and intelligent method to discover the similar images, which has been accomplished by using the
machine learning methods. In this paper, the multivariate feature descriptor method has been presented to extract the required
and relevant information from the large image databases. The proposed multivariate method involves the image color and texture
for the purpose of image matching to the query image (also known as a reference image). The most matching entities are returned
as the final results by the image extraction method. There are four methods, which involves three singular feature and one
multivariate feature based models, have been implemented. The multivariate model has been found much stable and returned
the maximum accuracy under this model.
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.
Content-based Image Retrieval System for an Image Gallery Search Application IJECEIAES
Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient browsing through three different search modes: retrieval by image query, retrieval by facial recognition, and retrieval by text or tags. In this project, the MPEG-7-like Powered Localized Color and Edge Directivity Descriptor is used to extract the feature vectors of the image database and the facial recognition system is built around the Eigenfaces concept. A graphical user interface with the basic functionality of an image gallery application is also developed to implement the three search modes. Results show that the application is able to retrieve and display images in a collection as thumbnail previews with high retrieval accuracy and medium relevance and the computational requirements for subsequent searches were significantly reduced through the incorporation of text-based image retrieval as one of the search modes. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.
In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.
Adversarial Photo Frame: Concealing Sensitive Scene Information in a User-Acc...multimediaeval
Paper: http://ceur-ws.org/Vol-2670/MediaEval_19_paper_24.pdf
Youtube: https://www.youtube.com/watch?v=keLM9fmKJSI
Zhuoran Liu and Zhengyu Zhao, Adversarial Photo Frame: Concealing Sensitive Scene Information of Social Images in a User-Acceptable Manner. Proc. of MediaEval 2018, 27-29 October 2019, Sophia Antipolis, France.
Abstract:
Personal privacy protection has become more and more crucial in the era of big multimedia data and artificial intelligence. This paper presents our submission to pixel privacy task, where we propose to fool the deep visual classification model that is for recognition of sensitive scenes by adding adversarial frame to the image. Experimental results indicate that our method can achieve strong adversarial effects while maintaining the visual appeal and social function of the transformed images.
Presented by Zhengyu Zhao
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
Similar to CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal
1. CAIR: Fast and Lightweight
Multi-Scale Color Attention Network
for Instagram Filter Removal
Woon-Ha Yeo, Wang-Taek Oh,
Kyung-Su Kang, Young-Il Kim, Han-Cheol Ryu*
AIM Workshop, ECCV 2022
2. Motivation
Image with filter
(Filtered image)
[2,5,7,11,25] show performance degradation due to filter.
“orange”
Image
“apple”
Deep
Learning
Model
Deep
Learning
Model
8. Nonlinear Activation Free Network (NAFNet) [4]
• Main architecture of NAFNet [4]
• NAFBlock [4]
Simplified channel attention (SCA)
Simple Gate
• Modules in NAFBlock [4]
9. CAIR: Multi-Scale Color Attention Network for Instagram Filter Removal
• Main architecture of proposed CAIR-Single
10. CAIR: Multi-Scale Color Attention Network for Instagram Filter Removal
• Main architecture of proposed CAIR-Multi • Color attention (CA) module inspired by [27]
14. [2] Bianco, Simone, et al. "Artistic photo filter removal using convolutional neural networks." Journal of Electronic Imaging 27.1 (2017): 011004.
[4] Chen, Liangyu, et al. "Simple baselines for image restoration." arXiv preprint arXiv:2204.04676 (2022).
[5] Chen, Yu-Hsiu, et al. "Filter-invariant image classification on social media photos." Proceedings of the 23rd ACM international conference on Multimedia. 2015.
[7] Hendrycks, Dan, and Thomas Dietterich. "Benchmarking neural network robustness to common corruptions and perturbations." arXiv preprint arXiv:1903.12261 (2019).
[11] Kinli, Furkan, Baris Ozcan, and Furkan Kirac. "Instagram filter removal on fashionable images." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
[12] Kınlı, Furkan, Barış Ö zcan, and Furkan Kıraç. "Patch-wise Contrastive Style Learning for Instagram Filter Removal." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
[14] Lim, Bee, et al. "Enhanced deep residual networks for single image super-resolution." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.
[25] Wu, Zhe, et al. "Recognizing instagram filtered images with feature de-stylization." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 07. 2020.
[27] Zamir, Syed Waqas, et al. "Cycleisp: Real image restoration via improved data synthesis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
References
15. For more details,
please visit our website:
Code: https://github.com/HnV-Lab/cair
Paper: https://arxiv.org/abs/2208.14039
![Motivation
Image with filter
(Filtered image)
[2,5,7,11,25] show performance degradation due to filter.
“orange”
Image
“apple”
Deep
Learning
Model
Deep
Learning
Model](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)