Color histograms have been considered to be effective for color image indexing and retrieval. However, the histogram only represents the global statistical color information. We propose a new method: A Spatial Weighted Color Histogram (SWCH), for image retrieval. The color space of a color image is partitioned into several color subsets according to hue, saturation and value in HSV color space. Then, the spatial center moment of each subset is calculated as the weight of the corresponding subset. Experiments show that our method is more effective in indexing color image and insensitive to intensity variations.

Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including singular value decomposition (SVD) and principal component analysis (PCA), have been proposed to reduce the dimensionality. In PCA, the dimensionality reduction is achieved by projecting the data to a subspace which contains most of the variance. As a common observation, the PCA basis function with the lowest frquency accounts for the highest variance. Therefore, the PCA subspace may not be the optimal one to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. The experimental results show that the proposed method (1) significantly reduces the feature dimensions compared with the original color histograms and (2) outperforms other dimension reduction techniques, namely the method based on SVD of quadratic matrix and PCA, in terms of retrieval accuracy.

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 principal component analysis (PCA) 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 the image retrievals of the 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 comparable accuracy.