Adaptive maximum global clustering based on coarse-fine scheme for human images

The main goal of segmentation is to partition an image into regions. Region-based segmentation is a technique for determining the region directly. Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this project, we propose a new method of segmenting an image into several sets of pixels with similar intensity values called regions. In this project, we propose a straightforward framework to automatically recover human bodies from color photos. Our method is made up of two procedures. First, we develop the adaptive global maximum Clustering.

In this procedure, we deal with an image histogram and automatically obtain the number of significant local maxima of the histogram. This number indicates the number of different regions in the image. Second, we detect a coarse torso (CT) using the multi cue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The performance of our algorithm is high compared to conventional methods.