Learning Binary Descriptors from Images
نویسندگان
چکیده
Binary descriptors have become popular for computer vision tasks because of their potential for smart phone applications. However, most binary descriptors have been heuristically hand-crafted. In this paper, we present a methodology to learn sparse binary descriptors from images. A new sampling and comparison pattern is also introduced and its advantages over the existing descriptors are discussed. We show experimental results for the task of matching pairs of images on the Patch dataset, using our descriptor MALCOM (Machine Learned Compact Descriptor). Results indicate that MALCOM’s performance surpasses that of FREAK, a stateof-the-art binary descriptor.
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تاریخ انتشار 2014