• 1. GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTSIEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEEBULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTSCELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.comClick Prediction for Web Image Reranking UsingMultimodal Sparse CodingABSTRACT:Image reranking is effective for improving the performance of a text-based imagesearch. However, existing reranking algorithms are limited for two main reasons:1) the textual meta-data associated with images is often mismatched with theiractual visual content and 2) the extracted visual features do not accurately describethe semantic similarities between images. Recently, user click information hasbeen used in image reranking, because clicks have been shown to more accuratelydescribe the relevance of retrieved images to search queries. However, a criticalproblem for click-based methods is the lack of click data, since only a smallnumber of web images have actually been clicked on by users. Therefore, we aimto solve this problem by predicting image clicks. We propose a multimodalhypergraph learning-based sparse coding method for image click prediction, andapply the obtained click data to the reranking of images. We adopt a hypergraph tobuild a group of manifolds, which explore the complementarity of differentfeatures through a group of weights. Unlike a graph that has an edge between twovertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve
  • 2. the local smoothness of the constructed sparse codes. An alternating optimizationprocedure is then performed, and the weights of different modalities and the sparsecodes are simultaneously obtained. Finally, a voting strategy is used to describe thepredicted click as a binary event (click or no click), from the images’corresponding sparse codes. Thorough empirical studies on a large-scale databaseincluding nearly 330K images demonstrate the effectiveness of our approach forclick prediction when compared with several other methods. Additional image re-rankingexperiments on real world data show the use of click prediction isbeneficial to improving the performance of prominent graph-based image re-rankingalgorithms.EXISTING SYSTEM:Most existing re-ranking methods use a tool known as pseudo-relevance feedback(PRF), where a proportion of the top-ranked images are assumed to be relevant,and subsequently used to build a model for re-ranking. This is in contrast torelevance feedback, where users explicitly provide feedback by labeling the topresults as positive or negative. In the classification-based PRF method, the top-rankedimages are regarded as pseudo-positive, and low-ranked images regarded aspseudo-negative examples to train a classifier, and then re-rank. Hsu et al. alsoadopt this pseudo-positive and pseudo-negative image method to develop aclustering-based re-ranking algorithm.DISADVANTAGES OF EXISTING SYSTEM:
  • 3.  One major problem impacting performance is the mismatches between theactual content of image and the textual data on the web page. The problem with these methods is the reliability of the obtained pseudo-positiveand pseudo-negative images is not guaranteed.PROPOSED SYSTEM:In this paper we propose a novel method named multimodal hyper graph learning-basedsparse coding for click prediction, and apply the predicted clicks to re-rankweb images. Both strategies of early and late fusion of multiple features are used inthis method through three main steps. We construct a web image base with associated click annotation, collectedfrom a commercial search engine. The search engine has recorded clicks foreach image. Indicate that the images with high clicks are strongly relevantto the queries, while present non-relevant images with zero clicks. These twocomponents form the image bases. We consider both early and late fusion in the proposed objective function.The early fusion is realized by directly concatenating multiple visualfeatures, and is applied in the sparse coding term. Late fusion isaccomplished in the manifold learning term. For web images without clicks,we implement hyper graph learning to construct a group of manifolds, whichpreserves local smoothness using hyper edges. Unlike a graph that has anedge between two vertices, a set of vertices are connected by the hyper edge
  • 4. in a hyper graph. Common graph-based learning methods usually onlyconsider the pair wise relationship between two vertices, ignoring thehigher-order relationship among three or more vertices. Using this term canhelp the proposed method preserve the local smoothness of the constructedsparse codes. Finally, an alternating optimization procedure is conducted to explore thecomplementary nature of different modalities. The weights of differentmodalities and the sparse codes are simultaneously obtained using thisoptimization strategy. A voting strategy is then adopted to predict if an inputimage will be clicked or not, based on its sparse code.ADVANTAGES OF PROPOSED SYSTEM: We effectively utilize search engine derived images annotated with c licks,and successfully predict the clicks for new input images without clicks.Based on the obtained clicks, we re-rank the images, a strategy which couldbe beneficial for improving commercial image searching. Second, we propose a novel method named mult imodal hyper graphlearning-based sparse coding. This method uses both early and late fusion inmultimodal learning. By simultaneously learning the sparse codes and theweights of different hyper graphs, the performance of sparse codingperforms significantly.
  • 5. SYSTEM ARCHITECTURE:SYSTEM REQUIREMENTS:HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb.
  • 6. SOFTWARE REQUIREMENTS: Operating system : Windows XP/7. Coding Language : ASP.net, C#.net Tool : Visual Studio 2010 Database : SQL SERVER 2008REFERENCE:Jun Yu, Member, IEEE, Yong Rui, Fellow, IEEE, and Dacheng Tao, SeniorMember, IEEE “Click Prediction for Web Image Reranking Using MultimodalSparse Coding” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23,NO. 5, MAY 2014
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    2014 IEEE JAVA IMAGE PROCESSING PROJECT Click prediction for web image reranking using multimodal sparse coding

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    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
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    • 1. GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTSIEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEEBULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTSCELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.comClick Prediction for Web Image Reranking UsingMultimodal Sparse CodingABSTRACT:Image reranking is effective for improving the performance of a text-based imagesearch. However, existing reranking algorithms are limited for two main reasons:1) the textual meta-data associated with images is often mismatched with theiractual visual content and 2) the extracted visual features do not accurately describethe semantic similarities between images. Recently, user click information hasbeen used in image reranking, because clicks have been shown to more accuratelydescribe the relevance of retrieved images to search queries. However, a criticalproblem for click-based methods is the lack of click data, since only a smallnumber of web images have actually been clicked on by users. Therefore, we aimto solve this problem by predicting image clicks. We propose a multimodalhypergraph learning-based sparse coding method for image click prediction, andapply the obtained click data to the reranking of images. We adopt a hypergraph tobuild a group of manifolds, which explore the complementarity of differentfeatures through a group of weights. Unlike a graph that has an edge between twovertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve
  • 2. the local smoothness of the constructed sparse codes. An alternating optimizationprocedure is then performed, and the weights of different modalities and the sparsecodes are simultaneously obtained. Finally, a voting strategy is used to describe thepredicted click as a binary event (click or no click), from the images’corresponding sparse codes. Thorough empirical studies on a large-scale databaseincluding nearly 330K images demonstrate the effectiveness of our approach forclick prediction when compared with several other methods. Additional image re-rankingexperiments on real world data show the use of click prediction isbeneficial to improving the performance of prominent graph-based image re-rankingalgorithms.EXISTING SYSTEM:Most existing re-ranking methods use a tool known as pseudo-relevance feedback(PRF), where a proportion of the top-ranked images are assumed to be relevant,and subsequently used to build a model for re-ranking. This is in contrast torelevance feedback, where users explicitly provide feedback by labeling the topresults as positive or negative. In the classification-based PRF method, the top-rankedimages are regarded as pseudo-positive, and low-ranked images regarded aspseudo-negative examples to train a classifier, and then re-rank. Hsu et al. alsoadopt this pseudo-positive and pseudo-negative image method to develop aclustering-based re-ranking algorithm.DISADVANTAGES OF EXISTING SYSTEM:
  • 3.  One major problem impacting performance is the mismatches between theactual content of image and the textual data on the web page. The problem with these methods is the reliability of the obtained pseudo-positiveand pseudo-negative images is not guaranteed.PROPOSED SYSTEM:In this paper we propose a novel method named multimodal hyper graph learning-basedsparse coding for click prediction, and apply the predicted clicks to re-rankweb images. Both strategies of early and late fusion of multiple features are used inthis method through three main steps. We construct a web image base with associated click annotation, collectedfrom a commercial search engine. The search engine has recorded clicks foreach image. Indicate that the images with high clicks are strongly relevantto the queries, while present non-relevant images with zero clicks. These twocomponents form the image bases. We consider both early and late fusion in the proposed objective function.The early fusion is realized by directly concatenating multiple visualfeatures, and is applied in the sparse coding term. Late fusion isaccomplished in the manifold learning term. For web images without clicks,we implement hyper graph learning to construct a group of manifolds, whichpreserves local smoothness using hyper edges. Unlike a graph that has anedge between two vertices, a set of vertices are connected by the hyper edge
  • 4. in a hyper graph. Common graph-based learning methods usually onlyconsider the pair wise relationship between two vertices, ignoring thehigher-order relationship among three or more vertices. Using this term canhelp the proposed method preserve the local smoothness of the constructedsparse codes. Finally, an alternating optimization procedure is conducted to explore thecomplementary nature of different modalities. The weights of differentmodalities and the sparse codes are simultaneously obtained using thisoptimization strategy. A voting strategy is then adopted to predict if an inputimage will be clicked or not, based on its sparse code.ADVANTAGES OF PROPOSED SYSTEM: We effectively utilize search engine derived images annotated with c licks,and successfully predict the clicks for new input images without clicks.Based on the obtained clicks, we re-rank the images, a strategy which couldbe beneficial for improving commercial image searching. Second, we propose a novel method named mult imodal hyper graphlearning-based sparse coding. This method uses both early and late fusion inmultimodal learning. By simultaneously learning the sparse codes and theweights of different hyper graphs, the performance of sparse codingperforms significantly.
  • 5. SYSTEM ARCHITECTURE:SYSTEM REQUIREMENTS:HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb.
  • 6. SOFTWARE REQUIREMENTS: Operating system : Windows XP/7. Coding Language : ASP.net, C#.net Tool : Visual Studio 2010 Database : SQL SERVER 2008REFERENCE:Jun Yu, Member, IEEE, Yong Rui, Fellow, IEEE, and Dacheng Tao, SeniorMember, IEEE “Click Prediction for Web Image Reranking Using MultimodalSparse Coding” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23,NO. 5, MAY 2014
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