Movie Scene Recognition Using Panoramic Frame and Representative Feature Patches

Recognizing scene information in images or videos, such as locating the objects and answering "Where am I?", has attracted much attention in computer vision 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.