Hierarchical object-oriented image and video segmentation algorithm based on 2D entropic thresholding
Jianping Fan, Gen Fujita, Jun Wei Yu, Koji Miyanohana, Takao Onoye, Nagisa Ishiura, Lide Wu, Isao Shirakawa · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
In this paper, a novel object-oriented hierarchical image and video segmentation algorithm is proposed based on 2D entropic thresholding, where the local variance contrast is selected for generating the 2D entropic surface because this parameter can indicate the strength of the edge accurately. The extracted object is first represented by a group of (4 X 4) blocks coarsely, then the intra-block edge extraction procedure and the joint spatiotemporal similarity test among neighboring blocks are further performed for determining the meaningful real objects. Experimental results have confirmed that the proposed hierarchical algorithm may be very useful for MPEG-4 applications, such as determining the Video Object Plane Formation automatically and selecting the coding pattern adaptively. A novel fast algorithm is also introduced for reducing the search burden. Moreover, this unsupervised algorithm also makes the automatic image and video segmentation possible.