IEEE Trans Image Process - Conjunctive patches subspace learning with side information for collaborative image retrieval.

Tópicos

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Resumo

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

Resumo Limpo

contentbas imag retriev cbir attract substanti attent past year potenti practic applic imag manag varieti relev feedback rf scheme design bridg semant gap lowlevel visual featur highlevel semant concept imag retriev task various collabor imag retriev cir scheme aim util user histor feedback log data similar dissimilar pairwis constraint improv perform cbir system howev exist subspac learn approach explicit label inform appli cir task although subspac learn techniqu play key role various comput vision task eg face recognit imag classif paper propos novel subspac learn framework ie conjunct patch subspac learn cpsl side inform learn effect semant subspac exploit user histor feedback log data cir task cpsl can effect integr discrimin inform label log imag geometr inform label log imag weak similar inform unlabel imag togeth learn reliabl subspac formal formul problem constrain optim problem present new subspac learn techniqu exploit user histor feedback log data extens experi synthet data set realworld imag databas demonstr effect propos scheme improv perform cbir system exploit user histor feedback log data

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