Image description using joint distribution of filter bank responses

This paper presents a unified framework for image descriptors based on quantized joint distribution of filter bank responses and evaluates the significance of filter bank and vector quantizer selection. First, a filter bank based representation of the local binary pattern (LBP) operator is introduced, which shows that LBP can also be presented as an operator producing vector quantized filter bank responses. Maximum response 8 (MR8) and Gabor filters are widely used alternatives to the derivative filters which are used to implement LBP, and the performance of these three sets is compared in the texture categorization and face recognition tasks. Despite their small spatial support, the local derivative filters are shown to outperform Gabor and MR8 filters in texture categorization with the KTH-TIPS2 images. In face recognition task with CMU PIE images, the Gabor filter-based representation achieves the best recognition rate. Furthermore, it is shown that when the filter response vectors are quantized for histogram based joint density estimation, thresholding is clearly faster than using learned codebooks and, being robust to gray-level changes, it yields better recognition rate in most cases. Third, automatic selection of filter bank is discussed and excellent face recognition performance in the face recognition task is achieved with the optimized filter bank.