IEEE Trans Pattern Anal Mach Intell - Local Pyramidal Descriptors for Image Recognition.

Tópicos

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Resumo

In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one's bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement. We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.

Resumo Limpo

paper present novel method improv flexibl descriptor match imag recognit use local multiresolut pyramid featur space propos imag patch repres multipl level descriptor detail level defin term local spatial pool resolut preserv multipl level detail local descriptor way hedg one bet level will relev match learn recognit introduc pyramid sift psift descriptor show use four stateoftheart imag recognit pipelin improv accuraci yield stateoftheart result techniqu applic independ spatial pyramid match show spatial pyramid can combin local pyramid obtain improv achiev stateoftheart result caltech caltech compar approach base sift featur intens imag techniqu effici extrem easi integr imag recognit pipelin

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