Category-Sensitive Hashing and Bloom Filter Based Descriptors for Online Keypoint Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

Abstract

In this paper we propose a method for learning a category-sensitive hash function (i.e. a hash function that tends to map inputs from the same category to the same hash bucket) and a feature descriptor based on the Bloom filter. Category-sensitive hash functions are robust to intra-category variation. In this paper we use them to produce descriptors that are invariant to transformations caused by for example viewpoint changes, lighting variation and deformation. Since the descriptors are based on Bloom filters, they support a ”union” operation. So descriptors of matched features can be aggregated by taking their union. We thus end up with one descriptor per keypoint instead of one descriptor per feature (By keypoint we refer to a world-space reference point and by feature we refer to an image-space interest point. Features are typically observations of keypoints and matched features are observations of the same keypoint). In short, the proposed descriptor has data-defined invariance properties due to the category-sensitive hashing and is aggregatable due to its Bloom filter inheritance. This is useful whenever we require custom invariance properties (e.g. tracking of deformable objects) and/or when we make multiple observations of each keypoint (e.g. tracking, multi-view stereo or visual SLAM).

Keywords

Keypoint recognition Feature matching Feature tracking Hashing Bloom filter 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CVAP/CSC, KTHStockholmSweden

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