Spatiotemporal segmentation using genetic algorithms
نویسندگان
چکیده
Segmentation is the process of identifying uniform regions based on certain conditions. Segmentation has been used for a long time in image analysis and computer vision for a variety of applications. In particular, there has been a growing interest in video sequence segmentation mainly due to the development of MPEG-4, which enables the content-based manipulation of multimedia data [1,2]. For this, the sequence must be "rst segmented into a set of meaningful objects [1,2]. However, since such objects are normally not characterized by a homogeneous intensity, color, or optical #ow, conventional segmentation algorithms using these features cannot produce meaningful partitions [1,2]. To overcome this problem, motion information has been used recently in many segmentation techniques, as one of the most important characteristics for identifying objects in a scene [1]. The methods that use motion can be divided into two main classes: joint motion estimation and segmentation, and spatiotemporal segmentation. The "rst approach estimates motion vectors at the pixel level and then clusters the pixels into regions of coherent motion, however, it has a major drawback: regions with coherent motion may contain multiple objects and need further segmentation for object extraction [1]. To overcome this drawback, the second approach uses information from both the spatial and temporal domain (i.e. color and motion), which produces a more meaningful segmentation [1,2]. This paper presents a new spatiotemporal segmentation of a video sequence. Each frame in a sequence is modeled using a Markov random "eld (MRF), which is e!ective in describing the spatial and temporal dependency of neighboring pixels and robust to degradation.
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عنوان ژورنال:
- Pattern Recognition
دوره 34 شماره
صفحات -
تاریخ انتشار 2001