Efficient quantization of color sift for image classification
Xiao Zhou, Cai-Zhi Zhu, Shin’ichi Satoh, Guo Yu-tang · 2011
The local feature (e.g. SIFT) and Bag of Words (BOW) model play key roles in achieving a state-of-the-art performance for image classification. Although we realize that utilizing extra color information will undoubtedly boost the local feature, there still have not been any research that have carefully focused on how to efficiently transfer this color boosted local feature into a boosted BOW. In this paper, a channel-wise separate quantization is newly proposed to replace the traditional multi-channel combined quantization for color SIFT. Dozens of experiments conducted on common data sets have consistently proven that the latter is noticeably worse than the former. We attribute this to a larger quantization error induced by the unreasonable assumption of identical feature distributions in multiple channels. In addition, we compare the distinctness of the intensity and color components separately, and the experimental results are beyond our expectations. Our experiments also show that the accuracy is noticeably enhanced when we take the top N nearest neighbors in soft assignment into account.