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Context-Based Support Vector Machines for Interconnected Image Annotation

Identifieur interne : 006465 ( Main/Exploration ); précédent : 006464; suivant : 006466

Context-Based Support Vector Machines for Interconnected Image Annotation

Auteurs : Hichem Sahbi [France] ; Xi Li [France, Australie, République populaire de Chine]

Source :

RBID : ISTEX:88D346215897958587666610EAD46396BBF0CD3B

Descripteurs français

English descriptors

Abstract

Abstract: We introduce in this paper a novel image annotation approach based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes (i) a variational approach which helps designing this function using both intrinsic features and the underlying contextual information resulting from different links and (ii) the proof of convergence of our kernel to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.

Url:
DOI: 10.1007/978-3-642-19315-6_17


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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