14 research outputs found
TREAT: Terse Rapid Edge-Anchored Tracklets
Fast computation, efficient memory storage, and
performance on par with standard state-of-the-art
descriptors make binary descriptors a convenient tool for
many computer vision applications. However their
development is mostly tailored for static images. To
respond to this limitation, we introduce TREAT (Terse
Rapid Edge-Anchored Tracklets), a new binary detector
and descriptor, based on tracklets. It harnesses moving
edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications
Efficient Discriminative Projections for Compact Binary Descriptors
Abstract. Binary descriptors of image patches are increasingly popular given that they require less storage and enable faster processing. This, however, comes at a price of lower recognition performances. To boost these performances, we project the image patches to a more discriminative subspace, and threshold their coordinates to build our binary descriptor. However, applying complex projections to the patches is slow, which negates some of the advantages of binary descriptors. Hence, our key idea is to learn the discriminative projections so that they can be decomposed into a small number of simple filters for which the responses can be computed fast. We show that with as few as 32 bits per descriptor we outperform the state-of-the-art binary descriptors in terms of both accuracy and efficiency.