NDLI: Semantic Subspace Projection and Its Applications in Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Jie Yu Qi Tian
Copyright Year	1991
Abstract	One of the most challenging problems for image retrieval applications is to find the optimal mapping between high-level semantic concept and low-level features. Traditional approaches often assume that images with same semantic label share strong visual similarities and should be clustered together to facilitate modeling and classification. Our research indicates this assumption is inappropriate in many cases. Instead we model the images as lying on nonlinear image subspaces embedded in the high-dimensional feature space and find that multiple subspaces may correspond to one semantic concept. By intelligently utilizing the similarity and dissimilarity information in semantic and geometric (image) domains, we propose an optimal semantic subspace projection (SSP) that captures the most important properties of the subspaces with respect to classification. Theoretical analysis proves that the well-known linear discriminant analysis (LDA) could be formulated as a special case of SSP. To capture the semantic concept dynamically, SSP can integrate relevance feedback efficiently through incremental learning. Kernel SSP is further proposed to handle nonlinearly separable data. Extensive experiments have been designed and conducted to compare our proposed method to the state-of-the-art techniques such as LDA, locality preservation projection (LPP), local linear embedding (LLE), local discriminant embedding (LDE) and their variants. The results show the superior performance of SSP.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	544
Ending Page	548
Page Count	5
File Size	348296
File Format	PDF
ISSN	10518215
Volume Number	18
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image retrieval Linear discriminant analysis Feedback Digital images Information retrieval Content based retrieval Computer science Space power stations Kernel Principal component analysis subspace learning linear discriminant analysis (LDA) principal component analysis relevance feedback semantic subspace projection (SSP) Linear Discriminant Analysis Semantic Subspace Projection Image Retrieval Relevance Feedback Subspace Learning Principal Component Analysis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Media Technology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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