Deep learned Inter-Channel Colored Texture Pattern: a new chromatic-texture descriptor

Texture is a dominant tool for feature extraction. Incorporation of inter-channel chromatic information along with the texture feature will improve the accuracy of feature extraction. This paper provides deep learned Inter-Channel Colored Texture Pattern which gives the inter-channel chromatic texture information of an image. The information is extracted individually from the co-occurrent pixel values of various channels. It affords the unique channel-wise information and its relation with neighboring pixel information of opponent space. Deep learning with convolutional neural network is applied for learning the feature based on color and texture. The experiments for content-based image retrieval are carried out on three different databases which vary in nature: CIFAR-10 dataset (DB1) (Krizhevsky in Learning multiple layers of features from tiny images, University of Toronto, 2009), Corel database (DB2) (Corel 1000 and Corel 10000 image database, http://wang.ist.psu.edu/docs/related.shtml) and Facescrub dataset (DB3) (Ng and Winkler in 2014 IEEE international conference on image processing (ICIP), pp 343–347, 2014). Facescrub dataset is used for face recognition. The experimental analysis by applying this descriptor provides considerable improvement from the previous works for content-based image retrieval and face recognition.