IEEE Trans Image Process - Coupled binary embedding for large-scale image retrieval.

Tópicos

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Resumo

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

Resumo Limpo

visual match crucial step imag retriev base bagofword bow model baselin method two keypoint consid match pair sift descriptor quantiz visual word howev sift visual word two limit first lose discrimin power quantiz second sift describ local textur featur drawback impair discrimin power bow model lead fals posit match tackl problem paper propos emb multipl binari featur index level model correl featur multiidf scheme introduc differ binari featur coupl invert file show match verif method base binari featur ham embed can effect incorpor framework extens explor fusion binari color featur imag retriev joint integr sift visual word binari featur great enhanc precis visual match reduc impact fals posit match method evalu extens experi four benchmark dataset ukbench holiday dupimag mir flickr m show method signific improv baselin approach addit largescal experi indic propos method requir accept memori usag queri time compar approach global color featur integr method yield competit perform stateoftheart

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