Sparsity Based Image Retrieval using relevance feedback
buir.contributor.author | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | |
dc.citation.epage | 2408 | en_US |
dc.citation.spage | 2405 | en_US |
dc.contributor.author | Günay, Osman | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.coverage.spatial | Orlando, FL, USA | en_US |
dc.date.accessioned | 2016-02-08T12:11:57Z | |
dc.date.available | 2016-02-08T12:11:57Z | |
dc.date.issued | 2012 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 30 Sept.-3 Oct. 2012 | en_US |
dc.description.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. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T12:11:57Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2012 | en |
dc.identifier.doi | 10.1109/ICIP.2012.6467382 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/28131 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICIP.2012.6467382 | en_US |
dc.source.title | 2012 19th IEEE International Conference on Image Processing | en_US |
dc.subject | BDA | en_US |
dc.subject | CBIR | en_US |
dc.subject | L1-ball | en_US |
dc.subject | Relevance Feedback | en_US |
dc.subject | Sparsity | en_US |
dc.subject | BDA | en_US |
dc.subject | CBIR | en_US |
dc.subject | L1-ball | en_US |
dc.subject | Relevance feedback | en_US |
dc.subject | Sparsity | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Discriminant analysis | en_US |
dc.subject | Eigenvalues and eigenfunctions | en_US |
dc.subject | Feedback | en_US |
dc.subject | Image processing | en_US |
dc.subject | Image retrieval | en_US |
dc.subject | Linear transformations | en_US |
dc.title | Sparsity Based Image Retrieval using relevance feedback | en_US |
dc.type | Conference Paper | en_US |
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