用BossaNova表示局部二进制描述符用于视觉识别

C. Caetano, S. Avila, S. Guimarães, A. Araújo
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引用次数: 18

摘要

二进制描述符最近在视觉识别任务中变得非常流行。这种流行很大程度上是由于它们的低复杂性,并且与非二进制描述符(如SIFT)相比具有相似的性能。在文献中,许多研究人员将二进制描述符与中级表示(例如,Bag-of-Words)结合使用。然而,尽管这些工作已经证明了有希望的结果,但它们的主要问题是由于使用了简单的中级表示和使用二元描述符,其中缺少旋转和尺度不变性。为了解决这些问题,我们建议在最近的中级表示(即BossaNova)中评估最先进的二进制描述符,即BRIEF, ORB, BRISK和FREAK,它丰富了Bag-of-Words模型,同时保留了二进制描述符信息。我们在具有挑战性的PASCAL VOC 2007数据集上进行的实验显示了出色的性能。此外,我们的方法在具有挑战性的色情检测的现实应用中显示出良好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Representing local binary descriptors with BossaNova for visual recognition
Binary descriptors have recently become very popular in visual recognition tasks. This popularity is largely due to their low complexity and for presenting similar performances when compared to non binary descriptors, like SIFT. In literature, many researchers have applied binary descriptors in conjunction with mid-level representations (e.g., Bag-of-Words). However, despite these works have demonstrated promising results, their main problems are due to use of a simple mid-level representation and the use of binary descriptors in which rotation and scale invariance are missing. In order to address those problems, we propose to evaluate state-of-the-art binary descriptors, namely BRIEF, ORB, BRISK and FREAK, in a recent mid-level representation, namely BossaNova, which enriches the Bag-of-Words model, while preserving the binary descriptor information. Our experiments carried out in the challenging PASCAL VOC 2007 dataset revealed outstanding performances. Also, our approach shows good results in the challenging real-world application of pornography detection.
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