Recognizing Clothes Patterns for Blind People by Confidence Margin based Feature Combination.

Clothes pattern recognition is a challenging task for blind or visually impaired people. Automatic clothes pattern recognition is also a challenging problem in computer vision due to the large pattern variations. In this paper, we present a new method to classify clothes patterns into 4 categories: stripe, lattice, special, and patternless. While existing texture analysis methods mainly focused on textures varying with distinctive pattern changes, they cannot achieve the same level of accuracy for clothes pattern recognition because of the large intra-class variations in each clothes pattern category. To solve this problem, we extract both structural feature and statistical feature from image wavelet subbands. Furthermore, we develop a new feature combination scheme based on the confidence margin of a classifier to combine the two types of features to form a novel local image descriptor in a compact and discriminative format. The recognition experiment is conducted on a database with 627 clothes images of 4 categories of patterns. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art texture analysis methods in the context of clothes pattern recognition.