Deep Saliency: Prediction of Interestingness in Video with CNN

Abstract

Deep Neural Networks have become winners in indexing of visual information. They have allowed achievement of better performances in the fundamental tasks of visual information indexing and retrieval such as image classification and object recognition. In fine-grain indexing tasks, namely object recognition in visual scenes, the CNNs classifiers have to evaluate multiple “object proposals”, that is windows in the image plane of different size and location. Hence the problem of recognition is coupled with the problem of localization. In this chapter a model of prediction of Areas-if-Interest in video on the basis of Deep CNNs is proposed. A Deep CNN architecture is designed to classify windows in salient and non-salient. Then dense saliency maps are built upon classification score results. Using the known sensitivity of human visual system (HVS) to residual motion, the usual primary features such as pixel colour values are completed with residual motion features. The experiments show that the choice of the input features for the Deep CNN depends on visual task: for the interest in dynamic content, the proposed model with residual motion is more efficient.