IEEE Trans Image Process - USB: ultrashort binary descriptor for fast visual matching and retrieval.

Tópicos

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Resumo

Currently, many local descriptors have been proposed to tackle a basic issue in computer vision: duplicate visual content matching. These descriptors either are represented as high-dimensional vectors relatively expensive to extract and compare or are binary codes limited in robustness. Bag-of-visual words (BoWs) model compresses local features into a compact representation that allows for fast matching and scalable indexing. However, the codebook training, high-dimensional feature extraction, and quantization significantly degrade the flexibility and efficiency of BoWs model. In this paper, we study an alternative to current local descriptors and BoWs model by extracting the ultrashort binary descriptor (USB) and a compact auxiliary spatial feature from each keypoint detected in images. A typical USB is a 24-bit binary descriptor, hence it directly quantizes visual clues of image keypoints to about 16 million unique IDs. USB allows fast image matching and indexing and avoids the expensive codebook training and feature quantization in BoWs model. The spatial feature complementarily captures the spatial configuration in neighbor region of each keypoint, hence is used to filter mismatched USBs in a cascade verification. In image matching task, USB shows promising accuracy and nearly one-order faster speed than SIFT. We also test USB in retrieval tasks on UKbench, Oxford5K, and 1.2 million distractor images. Comparisons with recent retrieval methods manifest the competitive accuracy, memory consumption, and significantly better efficiency of our approach.

Resumo Limpo

current mani local descriptor propos tackl basic issu comput vision duplic visual content match descriptor either repres highdimension vector relat expens extract compar binari code limit robust bagofvisu word bow model compress local featur compact represent allow fast match scalabl index howev codebook train highdimension featur extract quantiz signific degrad flexibl effici bow model paper studi altern current local descriptor bow model extract ultrashort binari descriptor usb compact auxiliari spatial featur keypoint detect imag typic usb bit binari descriptor henc direct quantiz visual clue imag keypoint million uniqu id usb allow fast imag match index avoid expens codebook train featur quantiz bow model spatial featur complementarili captur spatial configur neighbor region keypoint henc use filter mismatch usb cascad verif imag match task usb show promis accuraci near oneord faster speed sift also test usb retriev task ukbench oxfordk million distractor imag comparison recent retriev method manifest competit accuraci memori consumpt signific better effici approach

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