Video Summarization Using a Self-Growing and Self-Organized Neural Gas Network

In this paper, a novel method to generate video summaries is proposed, which is allocated mainly for being applied to on-line videos. The novelty of this approach lies in the fact that the video summarization problem is considered as a single query image retrieval problem. According to the proposed method, each frame is considered as a separate image and is described by the recently proposed Compact Composite Descriptors(CCDs) and a visual word histogram. In order to classify the frames into clusters, the method utilizes a powerful Self-Growing and Self-Organized Neural Gas (SGONG) network. Its main advantage is that it adjusts the number of created neurons and their topology in an automatic way. Thus, after training, the SGONG give us the appropriate number of output classes and their centers. The extraction of a representative key frame from every cluster leads to the generation of the video abstract. A significant characteristic of the proposed method is its ability to calculate dynamically the appropriate number of clusters. Experimental results are presented to indicate the effectiveness of the proposed approach.