Sparsity Based Image Retrieval using relevance feedback

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Sparsity Based Image Retrieval using relevance feedback.pdf (131.54 KB)

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2012

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Abstract

In this paper, a Content Based Image Retrieval (CBIR) algorithm employing relevance feedback is developed. After each round of user feedback Biased Discriminant Analysis (BDA) is utilized to find a transformation that best separates the positive samples from negative samples. The algorithm determines a sparse set of eigenvectors by L1 based optimization of the generalized eigenvalue problem arising in BDA for each feedback round. In this way, a transformation matrix is constructed using the sparse set of eigenvectors and a new feature space is formed by projecting the current features using the transformation matrix. Transformations developed using the sparse signal processing method provide better CBIR results and computational efficiency. Experimental results are presented. © 2012 IEEE.

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2012 19th IEEE International Conference on Image Processing

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IEEE

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Keywords

BDA, CBIR, L1-ball, Relevance Feedback, Sparsity, BDA, CBIR, L1-ball, Relevance feedback, Sparsity, Algorithms, Discriminant analysis, Eigenvalues and eigenfunctions, Feedback, Image processing, Image retrieval, Linear transformations

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http://hdl.handle.net/11693/28131

Published Version (Please cite this version)

http://dx.doi.org/10.1109/ICIP.2012.6467382

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Scholarly Publications - Electrical and Electronics Engineering

Language

English

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Conference Paper