Semantics In Digital Photos A Contenxtual Analysis
•Download as PPT, PDF•
1 like•549 views
Interpreting the semantics of an image is a hard problem. However, for storing and indexing large multimedia collections, it is essential to build systems that can automatically extract semantics from images. In this research we show how we can fuse content and context to extract semantics from digital photographs. Our experiments show that if we can properly model context associated with media, we can interpret semantics using only a part of high dimensional content data.
Report
Share
Report
Share
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Mind the Gap: Another look at the problem of the semantic gap in image retrieval
Multimedia Content Analysis, Management and Retrieval 2006, San Jose, California, USA, 17 - 19 Jan 2006
http://eprints.soton.ac.uk/261887/
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down.
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
Image and Video Retrieval: 5th International Conference, CIVR 2006, Tempe, AZ, USA, July 2006.
http://eprints.soton.ac.uk/262870/
This paper presents a novel technique for learning the underlying structure that links visual observations with semantics. The technique, inspired by a text-retrieval technique known as cross-language latent semantic indexing uses linear algebra to learn the semantic structure linking image features and keywords from a training set of annotated images. This structure can then be applied to unannotated images, thus providing the ability to search the unannotated images based on keyword. This factorisation approach is shown to perform well, even when using only simple global image features.
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and B...
Mastering the Gap: From Information Extraction to Semantic Representation / 3rd European Semantic Web Conference, Budva, Montenegro. May 2006.
http://eprints.soton.ac.uk/262737/
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches.
Multimodal Searching and Semantic Spaces: ...or how to find images of Dalmati...
Tutorial at the "Reality of the Semantic Gap in Image Retrieval" tutorial at the first international conference on Semantics And digital Media Technology (SAMT 2006). 6th December 2006.
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...
This document discusses using computational techniques to semantically retrieve unannotated images by enabling textual search of imagery without metadata. It describes:
1) Using exemplar image/metadata pairs to learn relationships between visual features and metadata, then projecting this to retrieve unannotated images.
2) Representing images as "visual terms" like words in text.
3) Creating a multidimensional "semantic space" where related images, terms and keywords are placed closely together based on training. This allows retrieving unannotated images that lie near descriptive keywords.
4) Experimental retrieval results on a Corel dataset, showing the approach works better for keywords associated with colors than others. The approach takes progress but significant challenges remain.
Sharp images and fuzzy concepts: Multimedia retrieval and the semantic gap
Talk for the University of Southampton IEEE Student Branch. 6th March 2012.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the work we have been involved with in the areas of multimedia analysis and search. The talk will start by looking at the broad range of multimedia analysis from low-level features to semantic understanding. This will be accompanied by demos of different multimedia analysis and search software developed over the years at Southampton.
We'll then explore the underpinnings of visual information analysis and see some computer vision techniques in action. In particular, we'll then explore how visual content can be represented in ways analogous to textual information and how techniques developed for analysing and indexing text can be adapted to images.
Finally, we'll look at how the next generation of multimedia analysis software is being developed, and introduce two open-source software projects being developed at Southampton that are paving the way for future research.
Searching Images: Recent research at Southampton
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
A brief introduction to extracting information from images
This document provides an introduction to extracting information from images. It discusses how images are represented digitally in computers and various techniques for extracting features from images, including lower-level features like color histograms and higher-level features like faces. Examples are given of feature extraction algorithms for faces and image composition. The document also discusses representing images using "bags of visual words" modeled after text analysis and introduces two open-source tools, OpenIMAJ and ImageTerrier, for image analysis.
Recommended
Mind the Gap: Another look at the problem of the semantic gap in image retrieval
Multimedia Content Analysis, Management and Retrieval 2006, San Jose, California, USA, 17 - 19 Jan 2006
http://eprints.soton.ac.uk/261887/
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down.
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
Image and Video Retrieval: 5th International Conference, CIVR 2006, Tempe, AZ, USA, July 2006.
http://eprints.soton.ac.uk/262870/
This paper presents a novel technique for learning the underlying structure that links visual observations with semantics. The technique, inspired by a text-retrieval technique known as cross-language latent semantic indexing uses linear algebra to learn the semantic structure linking image features and keywords from a training set of annotated images. This structure can then be applied to unannotated images, thus providing the ability to search the unannotated images based on keyword. This factorisation approach is shown to perform well, even when using only simple global image features.
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and B...
Mastering the Gap: From Information Extraction to Semantic Representation / 3rd European Semantic Web Conference, Budva, Montenegro. May 2006.
http://eprints.soton.ac.uk/262737/
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches.
Multimodal Searching and Semantic Spaces: ...or how to find images of Dalmati...
Tutorial at the "Reality of the Semantic Gap in Image Retrieval" tutorial at the first international conference on Semantics And digital Media Technology (SAMT 2006). 6th December 2006.
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...
This document discusses using computational techniques to semantically retrieve unannotated images by enabling textual search of imagery without metadata. It describes:
1) Using exemplar image/metadata pairs to learn relationships between visual features and metadata, then projecting this to retrieve unannotated images.
2) Representing images as "visual terms" like words in text.
3) Creating a multidimensional "semantic space" where related images, terms and keywords are placed closely together based on training. This allows retrieving unannotated images that lie near descriptive keywords.
4) Experimental retrieval results on a Corel dataset, showing the approach works better for keywords associated with colors than others. The approach takes progress but significant challenges remain.
Sharp images and fuzzy concepts: Multimedia retrieval and the semantic gap
Talk for the University of Southampton IEEE Student Branch. 6th March 2012.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the work we have been involved with in the areas of multimedia analysis and search. The talk will start by looking at the broad range of multimedia analysis from low-level features to semantic understanding. This will be accompanied by demos of different multimedia analysis and search software developed over the years at Southampton.
We'll then explore the underpinnings of visual information analysis and see some computer vision techniques in action. In particular, we'll then explore how visual content can be represented in ways analogous to textual information and how techniques developed for analysing and indexing text can be adapted to images.
Finally, we'll look at how the next generation of multimedia analysis software is being developed, and introduce two open-source software projects being developed at Southampton that are paving the way for future research.
Searching Images: Recent research at Southampton
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
A brief introduction to extracting information from images
This document provides an introduction to extracting information from images. It discusses how images are represented digitally in computers and various techniques for extracting features from images, including lower-level features like color histograms and higher-level features like faces. Examples are given of feature extraction algorithms for faces and image composition. The document also discusses representing images using "bags of visual words" modeled after text analysis and introduces two open-source tools, OpenIMAJ and ImageTerrier, for image analysis.
Das09112008
The document summarizes recent progress in techniques for extracting text objects from video documents. It discusses new and improved approaches proposed between 2003 and 2008 that have contributed to advances in the field. These include approaches that model text as hierarchical structures, use Gaussian mixture modeling of neighboring characters, exploit vertical edge features, and detect character strokes. The document also notes how former approaches have been enhanced by integrating more text characteristics and overcoming limitations.
BUILDING A SCALABLE MULTIMEDIA WEB OBSERVATORY
Web and Internet Science research group seminar series. University of Southampton. 13th March 2013.
The web is inherently multimedia in nature, and contains data and information in many different audio, visual and textual forms. To fully understand the nature of the web and the information contained within it, it is necessary to harness all modalities of data. Within the EU funded ARCOMEM project, we are building a platform for crawling and analysing samples of web and social-web data at scale. Whilst the project is ostensibly about issues related to intelligent web-archiving, the ARCOMEM software has features that make it ideal for use as a platform for a scalable Multimedia Web Observatory.
This talk will describe the ARCOMEM approach from data harvesting through to detailed content analysis and demonstrate how this approach relates to a multimedia web observatory. In addition to describing the overall framework, I'll show some of the research aspects of the system related specifically to multimodal multimedia data in small (>100GB) to medium-scale (multi-terabyte) web archives, and demonstrate how these are targeted to our Parliamentarian and Journalist end-users.
Multimedia content based retrieval slideshare.ppt
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
Geotagging Social Media Content with a Refined Language Modelling Approach
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
Geotagging Photographs By Sanjay Rana
Presentation proposes a formal approach to represent and analyse tag clouds, using the principles of cartography and graph theory.
Text extraction from natural scene image, a survey
Zhang, Honggang, et al. "Text extraction from natural scene image: A survey." Neurocomputing 122 (2013): 310-323.
Image search engine
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Searching Images: Recent research at Southampton
Information Retrieval group seminar series. The University of Glasgow. 21st February 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around Glasgow's Terrier software. The talk will also cover some of our recent work on image classification and image search result diversification.
Content Based Image Retrieval
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.
Image Indexing and Retrieval
This document discusses various approaches to image indexing and retrieval, including using text descriptions, extracting color, shape and texture features, compressed image data, and spatial relationships. It describes common techniques like color histograms, shape representations, texture features, and using DCT, wavelet or VQ compression. An integrated approach is recommended to support both textual queries and pictorial similarity comparisons.
Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlat...
Multimedia Content Access: Algorithms and Systems IV (SPIE Electronic Imaging 2010). January 2010.
http://eprints.soton.ac.uk/268496/
This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library.
Detecting text from natural images with Stroke Width Transform
in this presentation, one can know the text extraction method called stroke width transform and its advantages, disadvantages and applications.
Content Based Image and Video Retrieval Algorithm
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVM
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
Sub1547
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
Text Detection Strategies
This document summarizes different computer vision techniques for text detection:
- Stroke Width Transformation (SWT) uses edge detection and gradient maps to group pixels by similar stroke width into letter candidates. It can detect text of various sizes and styles but is relatively slow.
- Maximally Stable Extremal Regions (MSER) detects stable regions over a range of threshold values, suitable for character features. It can also detect text variations but is sensitive to blur.
- An alternative uses a sequential classifier trained on characters to directly find character regions instead of analyzing and rejecting components. This requires training on font styles but has faster performance.
[RakutenTechConf2013] [C4-1] Text detection in product images
1) The document describes Rakuten Institute of Technology's approach to text detection in product images.
2) It discusses current text detection methods and their shortcomings, such as the time-consuming nature of character/word annotation and difficulty detecting transparent text.
3) RIT's approach uses image-wise annotation to train a text image classifier, detects transparent text through adaptive edge detection and background recovery, and achieves more accurate and faster results than traditional methods.
A Survey on Approaches for Object Tracking
This document summarizes various approaches for object tracking in video sequences. It discusses common object detection methods like temporal differencing, optical flow and background subtraction. For object representation, it describes shape-based, motion-based, color-based and texture-based approaches. For object tracking, it analyzes target representation and localization methods like blob tracking and mean shift, as well as filtering and data association approaches like the Kalman filter and particle filter. It provides comparisons between Kalman and particle filters, and between contour tracking and visual feature matching methods. The document concludes that further research could improve computational efficiency and decrease tracking time for diverse video content.
Literature Review on Content Based Image Retrieval
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
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
This paper proposed a novel tree structure, DSTree, which can handle the stream data. The experiments show the comparable performance in terms of accuracy and efficiency.
Collaborative filtering with CCAM
This document describes a collaborative filtering approach using co-clustering with augmented data matrices (CCAM). CCAM extends a co-clustering algorithm based on information theory to simultaneously cluster users, items, and additional data (e.g. user profiles, item features). The authors apply CCAM to collaborative filtering by using the co-clusters as prototypes for predicting user ratings. They tune CCAM's parameters on a dataset from an online advertising site and compare its mean absolute error to other collaborative filtering methods. CCAM outperforms k-means clustering, k-nearest neighbors, and information-theoretic co-clustering on this task.
More Related Content
What's hot
Das09112008
The document summarizes recent progress in techniques for extracting text objects from video documents. It discusses new and improved approaches proposed between 2003 and 2008 that have contributed to advances in the field. These include approaches that model text as hierarchical structures, use Gaussian mixture modeling of neighboring characters, exploit vertical edge features, and detect character strokes. The document also notes how former approaches have been enhanced by integrating more text characteristics and overcoming limitations.
BUILDING A SCALABLE MULTIMEDIA WEB OBSERVATORY
Web and Internet Science research group seminar series. University of Southampton. 13th March 2013.
The web is inherently multimedia in nature, and contains data and information in many different audio, visual and textual forms. To fully understand the nature of the web and the information contained within it, it is necessary to harness all modalities of data. Within the EU funded ARCOMEM project, we are building a platform for crawling and analysing samples of web and social-web data at scale. Whilst the project is ostensibly about issues related to intelligent web-archiving, the ARCOMEM software has features that make it ideal for use as a platform for a scalable Multimedia Web Observatory.
This talk will describe the ARCOMEM approach from data harvesting through to detailed content analysis and demonstrate how this approach relates to a multimedia web observatory. In addition to describing the overall framework, I'll show some of the research aspects of the system related specifically to multimodal multimedia data in small (>100GB) to medium-scale (multi-terabyte) web archives, and demonstrate how these are targeted to our Parliamentarian and Journalist end-users.
Multimedia content based retrieval slideshare.ppt
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
Geotagging Social Media Content with a Refined Language Modelling Approach
Presentation of a geotagging approach for social media content with a refined language modelling approach. Presented at PAISI workshop, co-located with PA-KDD 2015, Ho Chi Minh City, Vietnam
Geotagging Photographs By Sanjay Rana
Presentation proposes a formal approach to represent and analyse tag clouds, using the principles of cartography and graph theory.
Text extraction from natural scene image, a survey
Zhang, Honggang, et al. "Text extraction from natural scene image: A survey." Neurocomputing 122 (2013): 310-323.
Image search engine
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Searching Images: Recent research at Southampton
Information Retrieval group seminar series. The University of Glasgow. 21st February 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around Glasgow's Terrier software. The talk will also cover some of our recent work on image classification and image search result diversification.
Content Based Image Retrieval
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.
Image Indexing and Retrieval
This document discusses various approaches to image indexing and retrieval, including using text descriptions, extracting color, shape and texture features, compressed image data, and spatial relationships. It describes common techniques like color histograms, shape representations, texture features, and using DCT, wavelet or VQ compression. An integrated approach is recommended to support both textual queries and pictorial similarity comparisons.
Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlat...
Multimedia Content Access: Algorithms and Systems IV (SPIE Electronic Imaging 2010). January 2010.
http://eprints.soton.ac.uk/268496/
This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library.
Detecting text from natural images with Stroke Width Transform
in this presentation, one can know the text extraction method called stroke width transform and its advantages, disadvantages and applications.
Content Based Image and Video Retrieval Algorithm
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVM
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
Sub1547
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
Text Detection Strategies
This document summarizes different computer vision techniques for text detection:
- Stroke Width Transformation (SWT) uses edge detection and gradient maps to group pixels by similar stroke width into letter candidates. It can detect text of various sizes and styles but is relatively slow.
- Maximally Stable Extremal Regions (MSER) detects stable regions over a range of threshold values, suitable for character features. It can also detect text variations but is sensitive to blur.
- An alternative uses a sequential classifier trained on characters to directly find character regions instead of analyzing and rejecting components. This requires training on font styles but has faster performance.
[RakutenTechConf2013] [C4-1] Text detection in product images
1) The document describes Rakuten Institute of Technology's approach to text detection in product images.
2) It discusses current text detection methods and their shortcomings, such as the time-consuming nature of character/word annotation and difficulty detecting transparent text.
3) RIT's approach uses image-wise annotation to train a text image classifier, detects transparent text through adaptive edge detection and background recovery, and achieves more accurate and faster results than traditional methods.
A Survey on Approaches for Object Tracking
This document summarizes various approaches for object tracking in video sequences. It discusses common object detection methods like temporal differencing, optical flow and background subtraction. For object representation, it describes shape-based, motion-based, color-based and texture-based approaches. For object tracking, it analyzes target representation and localization methods like blob tracking and mean shift, as well as filtering and data association approaches like the Kalman filter and particle filter. It provides comparisons between Kalman and particle filters, and between contour tracking and visual feature matching methods. The document concludes that further research could improve computational efficiency and decrease tracking time for diverse video content.
Literature Review on Content Based Image Retrieval
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
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
What's hot (20)
Geotagging Social Media Content with a Refined Language Modelling Approach
Geotagging Social Media Content with a Refined Language Modelling Approach
Text extraction from natural scene image, a survey
Text extraction from natural scene image, a survey
Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlat...
Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlat...
Detecting text from natural images with Stroke Width Transform
Detecting text from natural images with Stroke Width Transform
Real Time Myanmar Traffic Sign Recognition System using HOG and SVM
Real Time Myanmar Traffic Sign Recognition System using HOG and SVM
[RakutenTechConf2013] [C4-1] Text detection in product images
[RakutenTechConf2013] [C4-1] Text detection in product images
Literature Review on Content Based Image Retrieval
Literature Review on Content Based Image Retrieval
Viewers also liked
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
This paper proposed a novel tree structure, DSTree, which can handle the stream data. The experiments show the comparable performance in terms of accuracy and efficiency.
Collaborative filtering with CCAM
This document describes a collaborative filtering approach using co-clustering with augmented data matrices (CCAM). CCAM extends a co-clustering algorithm based on information theory to simultaneously cluster users, items, and additional data (e.g. user profiles, item features). The authors apply CCAM to collaborative filtering by using the co-clusters as prototypes for predicting user ratings. They tune CCAM's parameters on a dataset from an online advertising site and compare its mean absolute error to other collaborative filtering methods. CCAM outperforms k-means clustering, k-nearest neighbors, and information-theoretic co-clustering on this task.
Co-clustering with augmented data
Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of co-clustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Data Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked; both ads and users also have their feature data, i.e. the augmented data matrix. To evaluate the proposed method, we use two measures: classification accuracy and K-L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it consider the use of augmented data during clustering.
A scalable collaborative filtering framework based on co clustering
This document proposes a scalable collaborative filtering framework based on co-clustering. It introduces collaborative filtering and discusses limitations of existing methods. The framework uses co-clustering to simultaneously obtain user and item neighborhoods and generate predictions based on average ratings. Experimental results show the approach provides high quality predictions with lower computational cost than other methods.
Ch4.mapreduce algorithm design
Chapter 4 of Data-Intensive Text Processing with Map Reduce introduce the efficiently map-reduce algorithm, pairs and stripes. It show how to use these two algorithm to contrust the co-occurrence matrix. It compare the time complexity between pairs and stripes algorithms. According to the experiments, the stripes algorithm have the better efficiency than pairs algorithm.
Viewers also liked (6)
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
A scalable collaborative filtering framework based on co clustering
A scalable collaborative filtering framework based on co clustering
Similar to Semantics In Digital Photos A Contenxtual Analysis
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we present a model, which combined effective features of visual topics (global features over an image) and regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation. In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation. Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and databaseor web image search. Image annotation is a technique to choosing appropriate labels for images with extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the nnotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image
annotation.Experiments result on the 5k Corel dataset show the proposed method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.
A Study on Image Retrieval Features and Techniques with Various Combinations
This document discusses image retrieval techniques for content-based image retrieval systems. It begins with an introduction to the growth of digital image collections and the need for large-scale image retrieval systems. It then reviews different features used for image retrieval, such as color histograms, color moments, color coherence vectors, and discrete wavelet transforms. Edge features and corner features are also discussed. The document concludes that using only one feature type such as color or texture is not sufficient, and the best approach is to extract multiple high-quality features and combine them for image retrieval.
Region-based Semi-supervised Clustering Image Segmentation
Presentation of the paper:
Region-based semi-supervised clustering image segmentation
Tongfeng Sun; Zihui Ren; Shifei Ding
Natural Computation (ICNC), 2011 Seventh International Conference on , vol.4, no., pp.1855,1858, 26-28 July 2011
DOI: 10.1109/ICNC.2011.6022385
Dc31472476
This document summarizes research on using indexing techniques for efficient image retrieval. It discusses using content-based image retrieval (CBIR) to extract image features and store them for efficient comparison to query images. CBIR techniques described include color layout, edge histogram, scalable color, and relevance feedback to iteratively collect user feedback and improve retrieval performance over multiple cycles. The document also examines using various indexing and querying methods like semantic searching of image graphs to enhance image retrieval efficiency.
F010433136
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNING
The document describes a study on attention-based image captioning using deep learning. The study aims to generate image captions using an encoder-decoder model with an attention mechanism. The encoder is Google InceptionV3 which extracts image features, and the decoder is a GRU that generates captions. The model is trained on the MS COCO dataset and evaluated using BLEU score. Results show the attention mechanism helps focus on relevant image areas to produce descriptive captions.
Novel Hybrid Approach to Visual Concept Detection Using Image Annotation
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.
OBJECT DETECTION AND RECOGNITION: A SURVEY
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
A Review on Matching For Sketch Technique
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
Volume 2-issue-6-1974-1978
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Volume 2-issue-6-1974-1978
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Performance Evaluation Of Ontology And Fuzzybase Cbir
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
PERFORMANCE EVALUATION OF ONTOLOGY AND FUZZYBASE CBIR
IN THIS PAPER, WE HAVE DONE PERFORMANCE EVALUATION OF ONTOLOGY USING LOW-LEVEL FEATURES LIKE
COLOR, TEXTURE AND SHAPE BASED CBIR, WITH TOPIC SPECIFIC CBIR.THE RESULTING ONTOLOGY CAN BE USED
TO EXTRACT THE APPROPRIATE IMAGES FROM THE IMAGE DATABASE. RETRIEVING APPROPRIATE IMAGES FROM AN
IMAGE DATABASE IS ONE OF THE DIFFICULT TASKS IN MULTIMEDIA TECHNOLOGY. OUR RESULTS SHOW THAT THE
VALUES OF RECALL AND PRECISION CAN BE ENHANCED AND THIS ALSO SHOWS THAT SEMANTIC GAP CAN ALSO BE
REDUCED. THE PROPOSED ALGORITHM ALSO EXTRACTS THE TEXTURE VALUES FROM THE IMAGES AUTOMATICALLY
WITH ALSO ITS CATEGORY (LIKE SMOOTH, COURSE ETC) AS WELL AS ITS TECHNICAL INTERPRETATION.
IRJET- Image based Information Retrieval
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
Similar to Semantics In Digital Photos A Contenxtual Analysis (20)
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
A Study on Image Retrieval Features and Techniques with Various Combinations
A Study on Image Retrieval Features and Techniques with Various Combinations
Region-based Semi-supervised Clustering Image Segmentation
Region-based Semi-supervised Clustering Image Segmentation
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNING
ATTENTION BASED IMAGE CAPTIONING USING DEEP LEARNING
Novel Hybrid Approach to Visual Concept Detection Using Image Annotation
Novel Hybrid Approach to Visual Concept Detection Using Image Annotation
Performance Evaluation Of Ontology And Fuzzybase Cbir
Performance Evaluation Of Ontology And Fuzzybase Cbir
PERFORMANCE EVALUATION OF ONTOLOGY AND FUZZYBASE CBIR
PERFORMANCE EVALUATION OF ONTOLOGY AND FUZZYBASE CBIR
Recently uploaded
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
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.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Recently uploaded (20)
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Semantics In Digital Photos A Contenxtual Analysis
- 1. Semantics in Digital Photos: a Contenxtual Analysis Author / Pinaki Sinha, Ramesh Jain Conference / The IEEE International Conference on Semantic Computing, 2008, p58. – p.65 Presenter / Meng-Lun, Wu
- 25. Results (cont.)
Editor's Notes
- Global features: color, texture, shapes Local features: edges, salient points, objects
- B 是在像素特徵空間的隨機變數。