Information-Based Color Feature Representation for Image Classification

For the image classification task, the color histogram is widely used as an important color feature indicating the content of the image. However, the high-resolution color histograms are usually of high dimension and contain much redundant information which does not relate to the image content, while the low-resolution histograms cannot provide adequate discriminative information for image classification. In this paper, a new color feature representation is proposed which not only takes the correlation among neighbouring components of the conventional color histogram into account but removes the redundant information as well. A high-resolution, uniform quantized color histogram is first obtained from the image. Then the redundant bins are removed and some neighbouring bins are combined together to generate a new feature component to maximize the discriminative ability. The mutual information is adopted to evaluate the discriminative power of a specific feature set and an iterative algorithm is performed to derive the histogram quantization and their corresponding feature generation. To illustrate the effectiveness of the proposed feature representation, an application of detecting adult images, i.e., image classification between erotic and benign images, is carried out. Two widely used classification techniques, SVM and Adaboost, are employed as the classifier. Experimental results show the superior performance of our color representation compared with the conventional color histogram in image classification.