Learning to describe color composition of visual objects

Color composition is an important cue for image retrieval and object classification. In this paper we address the problem of inferring the color composition of visual objects from the pixel-level color distribution over the basic color terms. We build a discriminative model to tag each region with a dominant color and an associate one. We learn the human preference and cooccurrence patterns of the color names from weakly labeled real-world images. Experimental results on the ImageNet-Attribute data set and the Ebay data set show that our model can effectively describe the color composition of visual object in real-world images.