CHoG: Compressed Histogram of Gradients: A Low Bit-Rate Feature Descriptor

Establishing visual correspondences is an essential component of many computer vision problems, and is often done with robust, local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile distributed camera networks and large indexing problems. We propose a framework for computing low bit-rate feature descriptors with a 20£ reduction in bit rate. The framework is low complexity and has significant speed-up in the matching stage. We represent gradient histograms as tree structures which can be efficiently compressed. We show how to efficently compute distances between descriptors in their compressed representation eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Vijay Chandrasekhar (Stanford University), Gabriel Takacs (Stanford University), David Chen (Stanford University), Sam Tsai (Stanford University), Radek Grzeszczuk (Nokia Research Center), Bernd Girod (Stanford University)
• Keywords: Computer Vision, Correspondences, CVPR 2009, Feature Descriptors, Histogram Of Gradients