Colour appearance descriptors for image browsing and retrieval

In this paper, we focus on the development of whole scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: '' colour strength '', '' high/low lightness '' and '' multicoloured ''. Secondly we propose methods using-these. metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and Multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed, methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from, various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing.