Improving object color categorization with shapes
We explore the problem of object color categorization from natural images. Previous works use the histograms of RGB values of images with learning base methods. We propose to use shape information to help to localize the foreground areas of an image that determine the color of the object (such as car hoods), and focus the color learning and prediction on these areas. A novel Co-PLSA model is proposed to jointly learn the color and shape detectors in weakly supervised manner, where training images are only labeled with the color categories, while the locations of the foreground areas are not provided.