Information theory based region of interest extraction scheme with perceptual stimulus-response model

Nowadays, inspired by the behavior and neuronal architecture of human visual system (HVS), region of interest (ROI) detection methods are investigated by integrating several cognitive features. However, most of them are complex and time-consuming. To solve this problem, a video ROI extraction algorithm based on information theory and cognitive features is proposed in this paper. Based on the information theory, the spatial and temporal information are computed to measure the spatial and temporal content of video sequences respectively. Utilizing the visual features fusion strategy (VFFS), visual information is obtained from the combination of spatial and temporal information. Then, perceptual stimulus-response model (PSRM) is established to map the visual information to match the visual saliency. Regions with saliency scores, which are higher than a self-adaptive threshold, are regarded as ROIs. Experimental results show that the proposed scheme reduces 50% computation complexity, and can extract ROI more effective compared to Itti's method. Furthermore, the proposed ROI extraction scheme can be easily applied in the practical multimedia processing and pattern recognition system, such as video summarization.