BSIF: binarized statistical image features

by Juho Kannala and Esa Rahtu
Center for Machine Vision Research
University of Oulu

This is the website accompanying our paper:
Kannala J & Rahtu E: "BSIF: binarized statistical image features", ICPR 2012.


This paper proposes a method for constructing local image descriptors which efficiently encode texture information and are suitable for histogram based rep- resentation of image regions. The method computes a binary code for each pixel by linearly projecting local image patches onto a subspace, whose basis vectors are learnt from natural images via independent component analysis, and by binarizing the coordinates in this basis via thresholding. The length of the binary code string is determined by the number of basis vectors. Image re- gions can be conveniently represented by histograms of pixels' binary codes. Our method is inspired by other descriptors which produce binary codes, such as local binary pattern and local phase quantization. However, instead of heuristic code constructions, the proposed approach is based on statistics of natural images and this improves its modeling capacity. The experimental results show that our method improves accuracy in tex- ture recognition tasks compared to the state-of-the-art.



Code and data