Retrieval and Localization of Multiple Specific Objects with Hough Voting Based Ranking and A Contrario Decision


IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E96-A   No.12   pp.2717-2727
Publication Date: 2013/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E96.A.2717
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Vision
recognition,  localization,  detection,  image retrieval,  object retrieval,  scalable,  large scale,  bag of visual word,  Hough voting,  a contrario,  

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We present an algorithm for simultaneously recognizing and localizing planar textured objects in an image. The algorithm can scale efficiently with respect to a large number of objects added into the database. In contrast to the current state-of-the-art on large scale image search, our algorithm can accurately work with query images consisting of several specific objects and/or multiple instances of the same object. Our proposed algorithm consists of two major steps. The first step is to generate a set of hypotheses that provides information about the identities and the locations of objects in the image. To serve this purpose, we extend Bag-Of-Visual-Word (BOVW) image retrieval by incorporating a re-ranking scheme based on the Hough voting technique. Subsequently, in the second step, we propose a geometric verification algorithm based on A Contrario decision framework to draw out the final detection results from the generated hypotheses. We demonstrate the performance of the algorithm on the scenario of recognizing CD covers with a database consisting of more than ten thousand images of different CD covers. Our algorithm yield to the detection results of more than 90% precision and recall within a few seconds of processing time per image.