Spatio-temporal salient feature extraction for perceptual content based video retrieval

Video retrieval performance depends on many factors that may impact the output results in some respects. Among these factors, the selected features and the similarity function play prominent roles in the retrieval process. In this paper we propose a feature selection (FS) technique for content based video retrieval (CBVR). This scheme consists of several steps. First, the salient objects within video sequence are extracted through a segmentation process. These objects are described by spatio-temporal normalized features. Finally, during the query procedure, the derived features are compared to the recorded features database using Hausdorff distance matching. This study is carried out on a news video database. The performance of the proposed scheme in terms of recall and precision is evaluated and compared to existing algorithms. The experimental results clearly demonstrate that the proposed features are more accurate and robust for CBVR, than the basic spatio-temporal features.