Complex sport image classification using spatial color and posture context descriptors and neural classifiers

Searching a required image based on the content and visual meaning of the image is challenging. One of the complex groups of images is sport image since a sport image may contain various relevant information such as players' postures, textures and color of players' clothes, and complicated background ground. Achieving high recognition rate depends upond the features extracted from the image. In this paper, a new set of features is introduced. The features are 64-bin color histogram, single valued decomposition matrices for Cb and Cr color spaces, and 2-dimensional discrete cosine transformation. Regardless of any neural classifiers, our proposed features gives higher accurate results when compared with the other proposed features, i.e. edge histogram (EH) and region-based shape (RS).