An ICA-Based Illumination-Free texture model and its application to image retrieval

Abstract

We propose a novel pixel pattern-based approach for texture classification, which is independent of the variance of illumination. Gray scale images are first transformed into pattern maps in which edges and lines, used for characterizing texture information, are classified by pattern matching. We employ independent component analysis (ICA) which is widely applied to feature extraction. We use the basis functions learned through PCA as templates for pattern matching. Using PCA pattern maps, the feature vector is comprised of the numbers of the pixels belonging to a specific pattern. The effectiveness of the new feature is demonstrated by applications to image retrieval of Brodatz texture database. Comparisons with multichannel and multiresolution features indicate that the new feature is quite time saving, free of the influence of illumination, and has notable accuracy. The applicability of the proposed method to image retrieval has also been demonstrated.