摘要
Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering "Where am research field. Many existing scene recognition methods focus on static images, and cannot achieve satisfactory results on videos which contain more complex scenes features than images. In this paper, we propose a robust movie scene recognition approach based on panoramic frame and representative feature patch. More specifically, the movie is first efficiently segmented into video shots and scenes. Secondly, we introduce a novel key-frame extraction method using panoramic frame and also a local feature extraction process is applied to get the representative feature patches (RFPs) in each video shot. Thirdly, a Latent Dirichlet Allocation (LDA) based recognition model is trained to recognize the scene within each individual video scene clip. The correlations between video clips are considered to enhance the recognition performance. When our proposed approach is implemented to recognize the scene in realistic movies, the experimental results shows that it can achieve satisfactory performance.
Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering "Where am research field. Many existing scene recognition methods focus on static images, and cannot achieve satisfactory results on videos which contain more complex scenes features than images. In this paper, we propose a robust movie scene recognition approach based on panoramic frame and representative feature patch. More specifically, the movie is first efficiently segmented into video shots and scenes. Secondly, we introduce a novel key-frame extraction method using panoramic frame and also a local feature extraction process is applied to get the representative feature patches (RFPs) in each video shot. Thirdly, a Latent Dirichlet Allocation (LDA) based recognition model is trained to recognize the scene within each individual video scene clip. The correlations between video clips are considered to enhance the recognition performance. When our proposed approach is implemented to recognize the scene in realistic movies, the experimental results shows that it can achieve satisfactory performance.
基金
supported by the National Funds for Distinguished Young Scientists of China under Grant No.60925010
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20120005130002
the Cosponsored Project of Beijing Committee of Education,the Funds for Creative Research Groups of China under Grant No.61121001
the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No.IRT1049