A convolution kernel method for color recognition

Color recognition for out-door images is important for low-level computer vision, but it is a difficult task due to the effect of circumstances such as illumination, weather and so on. In this paper, we propose a novel convolution kernel method to extract color information from out-door images. When two images are compared, the proposed kernel maps images onto a high-dimentional feature space of which features are image fragments of two images and then the similarity between them is obtained through the inner-production of two image vectors. To evaluate the proposed kernel, it is applied to the vehicle color recognition problem. In the experiments on 500 vehicle images, the vehicle color recognition model with the proposed kernel shows about 92% of precision and 92% of recall. On the other hands, the model with a linear kernel shows about 45% of precision and 45% of recall. These experimental results imply that the proposed kernel is a plausible approach for the color recognition task.