Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval

In this paper, a novel algorithm is presented for the real-time, compressed-domain, unsupervised segmentation of image sequences and is applied to video indexing and retrieval. The segmentation algorithm uses motion and color information directly extracted from the MPEG-2 compressed stream. An iterative rejection scheme based on the bilinear motion model is used to effect foreground/background segmentation. Following that, meaningful foreground spatiotemporal objects are formed by initially examining the temporal consistency of the output of iterative rejection, clustering the resulting foreground macroblocks to connected regions and finally performing region tracking. Background segmentation to spatiotemporal objects is additionally performed. MPEG-7 compliant low-level descriptors describing the color, shape, position, and motion of the resulting spatiotemporal objects are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. This, combined with a relevance feedback mechanism, allows the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and the retrieval of relevant video segments. Desired spatial and temporal relationships between the objects in multiple-keyword queries can also be expressed, using the shot ontology. Experimental results of the application of the segmentation algorithm to known sequences demonstrate the efficiency of the proposed segmentation approach. Sample queries reveal the potential of employing this segmentation algorithm as part of an object-based video indexing and retrieval scheme.