Applying the multi-category learning to multiple video object extraction
Pattern Recognition
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We conceive the problem of multiple semantic video object (SVO) extraction as an issue of designing extensive operators on a complete lattice of partitions. As a result, we propose a framework based on spatial partition generation and application of optimal operators on the generated partitions. Based on a statistical analysis of the watershed algorithm, we develop a multivalued morphological spatial segmentation method that incorporates an edge-driven marker extraction algorithm and a growing method which integrates both color and edge information. Having embedded the problem in the partition lattice framework, we propose a spatio-temporal regional maximum likelihood operator for extraction purposes. Some theoretical properties of the operator are established. Experimental results on several MPEG-4 test video sequences show that our scheme improves the precision of the extracted SVO boundaries compared to traditional watershed algorithms and provides accurate tracking of multiple SVOs in both static and moving camera scenarios. Furthermore, this scheme can be extended to deal with more general interactive video authoring systems