For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
Image Search by using various Reranking MethodsIRJET Journal
This document discusses various image reranking methods that can be used to improve image search results. It provides an overview of different reranking approaches such as clustering-based methods, classification-based methods, and graph-based methods. For each category, several specific reranking techniques are described and their advantages and limitations are discussed. The purpose of the document is to review existing image reranking methods and provide a comparative analysis to help researchers develop more effective image search systems.
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
ENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURESIAEME Publication
Several commercial search engines adopt Image re- ranking approach to enhance the quality of results for web based image search .When an user issues a query keyword, the search engine first selects a group of images based on textual information. If the user selects a query image from the retrieved group, the rest of the images are re-ranked based on their visual similarities with the query image. The images with similar visual features cannot be correlated. Also learning a universal visual semantic space which depicts the characteristics of images which are highly different to each other is a tedious task.
Mind finder: interactive sketch-based image search on millions of imagesNammin Lee
MindFinder is an interactive sketch-based image search system that allows users to search over 2 million images using sketches, tags, and colors. It addresses limitations of prior sketch-based search by representing images as salient curves matched to user sketches. The system indexes sketches, tags, and colors for real-time multimodal search. An evaluation found sketch-based search to be more accurate and convenient than text for specific intents, demonstrating MindFinder's ability to help users better express search needs through simple sketches.
image caption generation using deep learningKnaresh101
An overview of a deep learning project to generate captions for images using convolutional and recurrent neural networks. The goal is to automate image captioning to make image-heavy content more accessible for the visually impaired and to improve search engine optimization and social media post creation. The research aims to fill gaps and have unprecedented benefits for assistive technology, augmented reality, and e-commerce.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
Image Search by using various Reranking MethodsIRJET Journal
This document discusses various image reranking methods that can be used to improve image search results. It provides an overview of different reranking approaches such as clustering-based methods, classification-based methods, and graph-based methods. For each category, several specific reranking techniques are described and their advantages and limitations are discussed. The purpose of the document is to review existing image reranking methods and provide a comparative analysis to help researchers develop more effective image search systems.
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
ENHANCED WEB IMAGE RE-RANKING USING SEMANTIC SIGNATURESIAEME Publication
Several commercial search engines adopt Image re- ranking approach to enhance the quality of results for web based image search .When an user issues a query keyword, the search engine first selects a group of images based on textual information. If the user selects a query image from the retrieved group, the rest of the images are re-ranked based on their visual similarities with the query image. The images with similar visual features cannot be correlated. Also learning a universal visual semantic space which depicts the characteristics of images which are highly different to each other is a tedious task.
Mind finder: interactive sketch-based image search on millions of imagesNammin Lee
MindFinder is an interactive sketch-based image search system that allows users to search over 2 million images using sketches, tags, and colors. It addresses limitations of prior sketch-based search by representing images as salient curves matched to user sketches. The system indexes sketches, tags, and colors for real-time multimodal search. An evaluation found sketch-based search to be more accurate and convenient than text for specific intents, demonstrating MindFinder's ability to help users better express search needs through simple sketches.
image caption generation using deep learningKnaresh101
An overview of a deep learning project to generate captions for images using convolutional and recurrent neural networks. The goal is to automate image captioning to make image-heavy content more accessible for the visually impaired and to improve search engine optimization and social media post creation. The research aims to fill gaps and have unprecedented benefits for assistive technology, augmented reality, and e-commerce.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
Image Based Information Retrieval Using Deep Learning and Clustering TechniquesIRJET Journal
This document summarizes an approach for image-based information retrieval using deep learning and clustering techniques. It begins by discussing how current search engines rely on text-based methods that cannot fully capture image content. The proposed approach uses deep learning to extract visual features from images and hierarchical clustering to organize similar images. Images are initially retrieved based on text queries, then re-ranked based on visual relevance scores to return only images truly relevant to the user's query. The approach was found to reduce the semantic gap between low-level image features and high-level semantics compared to traditional text-based search.
Image Based Information Retrieval Using Deep Learning and Clustering TechniquesIRJET Journal
This document summarizes an approach for image-based information retrieval using deep learning and clustering techniques. It begins by discussing how current search engines rely on text-based approaches that have limitations. The proposed approach uses deep learning to extract visual features from images and hierarchical clustering to organize similar images. Images are initially retrieved based on a user query, then re-ranked based on computed relevance scores to return more relevant results. The approach was found to reduce the semantic gap compared to text-based methods by leveraging visual features from images.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
Webmarketing123 webinar: Google's NEW Image-Based Search And Its Impact on Yo...Webmarketing123
Google has released new image-based search and voice search features that allow users to conduct searches using images or voice commands from desktop computers. This will impact search behavior and SEO strategies. Marketers should optimize websites and images to be relevant for these new search methods by including targeted keywords in alt text, descriptions and content. Faster search speeds mean results above the fold will be more important. Keeping up with advances in mobile search and Google's algorithm is key to remaining successful.
Searching based on an image file as opposed to keywords is one of Google's latest and greatest developments for Instant Search. If you give a search engine an image file, Chrome is going to answer your query. Speeding forward with the wider release of voice search, Google has now made this feature available in two thirds of the world's mobile population.
Chrome on mobile is also showing off a new look with slick, quick clickable search icons and a slideshow search results view that feels a little like iTunes. Google's release of "Instant Pages" in Chrome Beta will be the biggest sprint yet to save searchers more time than ever before.
How will this affect you? In Webmarketing123's upcoming webinar, Google's NEW Image-Based Search & Its Impact on your SEO, you'll learn how:
- Google's new developments will affect future search behavior
- You can utilize images for SEO benefits
- Google's faster algorithm speed will affect your current SEO programs
- You can optimally position your site for Google Instant Pages
A soft computing approach for image searching using visual rerankingIAEME Publication
The document discusses image search and ranking techniques. It describes how current search engines primarily use text-based retrieval, which can return many irrelevant images. The document then proposes a visual reranking approach to re-order images based on visual similarity analysis to improve search results. Key aspects of the approach include extracting image features, analyzing visual similarities between images, and leveraging these similarities to provide a reranked output with more relevant images prioritized.
This document describes a proposed content-based image retrieval system that uses histogram values and user feedback to effectively search and rank image search results. The proposed system tracks user navigation patterns and stores user feedback information separately from image data to improve search performance. Images are ranked based on their histogram values, user feedback, text metadata, and number of clicks to better capture user intent than existing keyword-based search systems. This approach aims to address challenges with existing CBIR systems such as helping users refine image queries and reducing information overload through improved query understanding and result ranking.
This paper discusses research into understanding user search behavior when interacting with search result pages. It notes that while search engines optimize for metrics like speed and ranking quality, users' perceptions are influenced by how easily results can be scanned. Eye tracking experiments have shown that users make decisions within seconds based mainly on snippets and URLs. The paper proposes developing a comprehensive model of user behavior by combining analysis of query logs, toolbar clickstream data, and eye tracking experiments to better optimize search engine design and metrics.
A Review on User Personalized Tag Based Image Search by Tag RelevanceIRJET Journal
This document reviews approaches for user personalized tag-based image search. It discusses how tag-based image retrieval (TBIR) uses manually assigned tags to represent and search for images, as opposed to content-based image retrieval (CBIR) which matches images based on visual similarity. The document proposes a system that combines TBIR with CBIR, using both semantic tags and visual features to rank images for a user. It also discusses using clustering, common tags, and user profiles to filter images during the retrieval process. Experimental results showed that incorporating semantic and visual information improves diversity in the retrieved results compared to tag-based retrieval alone.
IRJET-A Review on User Personalized Tag Based Image Search by Tag RelevanceIRJET Journal
This document reviews approaches for user personalized tag-based image search. It discusses how tag-based image retrieval (TBIR) uses manually assigned tags to represent images, as opposed to content-based image retrieval (CBIR) which matches images based on visual similarity. The paper proposes a system that combines TBIR with CBIR, using semantic and visual features to re-rank images for improved results. It also discusses using social media image tagging and geo-tagging techniques to better organize large image collections for search. The system architecture incorporates tagging, clustering, indexing and both online and offline components to perform user personalized tag-based image search.
Social Re-Ranking using Tag Based Image SearchIRJET Journal
This document proposes a social re-ranking system for tag-based image retrieval from social media datasets. It first retrieves images based on keyword matching of the query tags. It then applies three re-ranking steps: 1) Inter-user ranking to rank images by user contributions to the query, 2) Time stamp ranking to prioritize more recent images based on title and timestamp, 3) View ranking to improve relevance by considering image view counts. An experiment on a social image dataset showed the method effectively and efficiently retrieves more relevant and diverse images compared to other tag-based image ranking methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS Web image re ranking using query-specif...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Web image re ranking using query-sp...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
Image Based Information Retrieval Using Deep Learning and Clustering TechniquesIRJET Journal
This document summarizes an approach for image-based information retrieval using deep learning and clustering techniques. It begins by discussing how current search engines rely on text-based methods that cannot fully capture image content. The proposed approach uses deep learning to extract visual features from images and hierarchical clustering to organize similar images. Images are initially retrieved based on text queries, then re-ranked based on visual relevance scores to return only images truly relevant to the user's query. The approach was found to reduce the semantic gap between low-level image features and high-level semantics compared to traditional text-based search.
Image Based Information Retrieval Using Deep Learning and Clustering TechniquesIRJET Journal
This document summarizes an approach for image-based information retrieval using deep learning and clustering techniques. It begins by discussing how current search engines rely on text-based approaches that have limitations. The proposed approach uses deep learning to extract visual features from images and hierarchical clustering to organize similar images. Images are initially retrieved based on a user query, then re-ranked based on computed relevance scores to return more relevant results. The approach was found to reduce the semantic gap compared to text-based methods by leveraging visual features from images.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
Webmarketing123 webinar: Google's NEW Image-Based Search And Its Impact on Yo...Webmarketing123
Google has released new image-based search and voice search features that allow users to conduct searches using images or voice commands from desktop computers. This will impact search behavior and SEO strategies. Marketers should optimize websites and images to be relevant for these new search methods by including targeted keywords in alt text, descriptions and content. Faster search speeds mean results above the fold will be more important. Keeping up with advances in mobile search and Google's algorithm is key to remaining successful.
Searching based on an image file as opposed to keywords is one of Google's latest and greatest developments for Instant Search. If you give a search engine an image file, Chrome is going to answer your query. Speeding forward with the wider release of voice search, Google has now made this feature available in two thirds of the world's mobile population.
Chrome on mobile is also showing off a new look with slick, quick clickable search icons and a slideshow search results view that feels a little like iTunes. Google's release of "Instant Pages" in Chrome Beta will be the biggest sprint yet to save searchers more time than ever before.
How will this affect you? In Webmarketing123's upcoming webinar, Google's NEW Image-Based Search & Its Impact on your SEO, you'll learn how:
- Google's new developments will affect future search behavior
- You can utilize images for SEO benefits
- Google's faster algorithm speed will affect your current SEO programs
- You can optimally position your site for Google Instant Pages
A soft computing approach for image searching using visual rerankingIAEME Publication
The document discusses image search and ranking techniques. It describes how current search engines primarily use text-based retrieval, which can return many irrelevant images. The document then proposes a visual reranking approach to re-order images based on visual similarity analysis to improve search results. Key aspects of the approach include extracting image features, analyzing visual similarities between images, and leveraging these similarities to provide a reranked output with more relevant images prioritized.
This document describes a proposed content-based image retrieval system that uses histogram values and user feedback to effectively search and rank image search results. The proposed system tracks user navigation patterns and stores user feedback information separately from image data to improve search performance. Images are ranked based on their histogram values, user feedback, text metadata, and number of clicks to better capture user intent than existing keyword-based search systems. This approach aims to address challenges with existing CBIR systems such as helping users refine image queries and reducing information overload through improved query understanding and result ranking.
This paper discusses research into understanding user search behavior when interacting with search result pages. It notes that while search engines optimize for metrics like speed and ranking quality, users' perceptions are influenced by how easily results can be scanned. Eye tracking experiments have shown that users make decisions within seconds based mainly on snippets and URLs. The paper proposes developing a comprehensive model of user behavior by combining analysis of query logs, toolbar clickstream data, and eye tracking experiments to better optimize search engine design and metrics.
A Review on User Personalized Tag Based Image Search by Tag RelevanceIRJET Journal
This document reviews approaches for user personalized tag-based image search. It discusses how tag-based image retrieval (TBIR) uses manually assigned tags to represent and search for images, as opposed to content-based image retrieval (CBIR) which matches images based on visual similarity. The document proposes a system that combines TBIR with CBIR, using both semantic tags and visual features to rank images for a user. It also discusses using clustering, common tags, and user profiles to filter images during the retrieval process. Experimental results showed that incorporating semantic and visual information improves diversity in the retrieved results compared to tag-based retrieval alone.
IRJET-A Review on User Personalized Tag Based Image Search by Tag RelevanceIRJET Journal
This document reviews approaches for user personalized tag-based image search. It discusses how tag-based image retrieval (TBIR) uses manually assigned tags to represent images, as opposed to content-based image retrieval (CBIR) which matches images based on visual similarity. The paper proposes a system that combines TBIR with CBIR, using semantic and visual features to re-rank images for improved results. It also discusses using social media image tagging and geo-tagging techniques to better organize large image collections for search. The system architecture incorporates tagging, clustering, indexing and both online and offline components to perform user personalized tag-based image search.
Social Re-Ranking using Tag Based Image SearchIRJET Journal
This document proposes a social re-ranking system for tag-based image retrieval from social media datasets. It first retrieves images based on keyword matching of the query tags. It then applies three re-ranking steps: 1) Inter-user ranking to rank images by user contributions to the query, 2) Time stamp ranking to prioritize more recent images based on title and timestamp, 3) View ranking to improve relevance by considering image view counts. An experiment on a social image dataset showed the method effectively and efficiently retrieves more relevant and diverse images compared to other tag-based image ranking methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS Web image re ranking using query-specif...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Web image re ranking using query-sp...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
18
1. Impulse Technologies
Beacons U to World of technology
044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
IntentSearch: Capturing User Intention for One-Click Internet
Image Search
Abstract
1
Web-scale image search engines (e.g., Google image search, Bing image search)
mostly rely on surrounding text features. It is difficult for them to interpret users' search
intention only by query keywords and this leads to ambiguous and noisy search results
which are far from satisfactory. It is important to use visual information in order to solve
the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet
image search approach. It only requires the user to click on one query image with
minimum effort and images from a pool retrieved by text-based search are reranked
based on both visual and textual content. Our key contribution is to capture the users'
search intention from this one-click query image in four steps. 1) The query image is
categorized into one of the predefined adaptive weight categories which reflect users'
search intention at a coarse level. Inside each category, a specific weight schema is used
to combine visual features adaptive to this kind of image to better rerank the text-based
search result. 2) Based on the visual content of the query image selected by the user and
through image clustering, query keywords are expanded to capture user intention. 3)
Expanded keywords are used to enlarge the image pool to contain more relevant images.
4) Expanded keywords are also used to expand the query image to multiple positive
visual examples from which new query specific visual and textual similarity metrics are
learned to further improve content-based image reranking. All these steps are automatic,
without extra effort from the user. This is critically important for any commercial web-
based image search engine, where the user interface has to be extremely simple. Besides
this key contribution, a set of visual features which are both effective and efficient in
Internet image search are designed. Experimental evaluation shows that our approach
significantly improves the precision of top-ranked images and also the user experi- nce.
1
Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
1