Perception-oriented prominent region detection in video sequences using fuzzy inference neural network

Authors:
Congyan Lang;De Xu;Xu Yang;Yiwei Jiang;Wengang Cheng
Affiliations:
Dept. of Computer Science, Beijing Jiaotong University, Beijing, China;Dept. of Computer Science, Beijing Jiaotong University, Beijing, China;Dept. of Computer Science, Beijing Jiaotong University, Beijing, China;Dept. of Computer Science, Beijing Jiaotong University, Beijing, China;Dept. of Computer Science, Beijing Jiaotong University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. Firstly, we partition each frame into homogeneous regions using a technique based on a nonparameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.