IEEE Trans Pattern Anal Mach Intell - Online Multiple Kernel Similarity Learning for Visual Search.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ featur(1941) imag(1645) propos(1176) }
{ method(1557) propos(1049) approach(1037) }
{ framework(1458) process(801) describ(734) }
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Resumo

Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in Content-Based Image Retrieval (CBIR). Despite their popularity and success, most existing methods on distance metric learning are limited in two aspects. First, they typically assume the target proximity function follows the family of Mahalanobis distances, which limits their capacity of measuring similarity of complex patterns in real applications. Second, they often cannot effectively handle the similarity measure of multi-modal data that may originate from multiple resources. To overcome these limitations, this paper investigates an online kernel ranking framework for learning kernel-based proximity functions, which goes beyond the conventional linear distance metric learning approaches. Based on the framework, we propose a novel Online Multiple Kernel Ranking (OMKR) method, which learns a flexible nonlinear proximity function with multiple kernels to improve visual similarity search in CBIR. We evaluate the proposed technique for CBIR on a variety of image data sets, in which encouraging results show that OMKR outperforms the state-of-the-art techniques significantly.

Resumo Limpo

recent year wit number studi distanc metric learn improv visual similar search contentbas imag retriev cbir despit popular success exist method distanc metric learn limit two aspect first typic assum target proxim function follow famili mahalanobi distanc limit capac measur similar complex pattern real applic second often effect handl similar measur multimod data may origin multipl resourc overcom limit paper investig onlin kernel rank framework learn kernelbas proxim function goe beyond convent linear distanc metric learn approach base framework propos novel onlin multipl kernel rank omkr method learn flexibl nonlinear proxim function multipl kernel improv visual similar search cbir evalu propos techniqu cbir varieti imag data set encourag result show omkr outperform stateoftheart techniqu signific

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