IEEE Trans Pattern Anal Mach Intell - Spatially-Constrained Similarity Measure for Large-Scale Object Retrieval.

Tópicos

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{ motion(1329) object(1292) video(1091) }
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Resumo

One fundamental problem in object retrieval with the bag-of-words model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the model, most of them are either too strict or too loose so that they are only effective in limited cases. In this paper, a new spatially-constrained similarity measure (SCSM) is proposed to handle object rotation, scaling, view point change and appearance deformation. The similarity measure can be efficiently calculated by a voting-based method using inverted files. During the retrieval process, object localization in the database images can also be simultaneously achieved. Furthermore, based on the retrieval and localization results of SCSM, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial search results. Extensive performance evaluations show that SCSM significantly outperforms other spatial models including RANSAC-based spatial verification, and achieves the state-of-art with k-NN re-ranking. We also adapted SCSM for mobile product image search with an iterative algorithm to simultaneously extract the product instance from the mobile query image, and retrieve visually similar product images. Experiments show that our approach can robustly localize and extract the product in the query image, and hence drastically improve the retrieval accuracy over baseline methods.

Resumo Limpo

one fundament problem object retriev bagofword model lack spatial inform although various approach propos incorpor spatial constraint model either strict loos effect limit case paper new spatiallyconstrain similar measur scsm propos handl object rotat scale view point chang appear deform similar measur can effici calcul votingbas method use invert file retriev process object local databas imag can also simultan achiev furthermor base retriev local result scsm introduc novel robust rerank method knearest neighbor queri automat refin initi search result extens perform evalu show scsm signific outperform spatial model includ ransacbas spatial verif achiev stateofart knn rerank also adapt scsm mobil product imag search iter algorithm simultan extract product instanc mobil queri imag retriev visual similar product imag experi show approach can robust local extract product queri imag henc drastic improv retriev accuraci baselin method

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