Object detection and segmentation by composition of fast fuzzy C-mean clustering based maps

Mehmood Nawaz, Rizwan Qureshi, Mansoor Ali Teevno, Ali Raza Shahid

Abstract: The extraction of salient objects from a cluttered background without any prior knowledge is a challenging task in salient object detection and segmentation. A salient object can be detected from the uniqueness, rarity, or unproductivity of the salient regions in an image. However, an object with a similar color appearance may have a marginal visual divergence that is even difficult for the human eyes to recognize. In this paper, we propose a technique which compose and fuse the fast fuzzy c-mean (FFCM) clustering saliency maps to separate the salient object from the background in the image. To be specific, we first generate the maps using FFCM clustering, that contain specific parts of the salient region, which are composed later by using the Porter–Duff composition method. Outliers in the extracted salient regions are removed using a morphological technique in the post-processing step. To extract the final map from the initially constructed blended maps, we use a fused mask, which is the composite form of color prior, location prior, and frequency prior. Experiment results on six public data sets (MSRA, THUR-15000, MSRA-10K, HKU-IS, DUT-OMRON, and SED) clearly show the efficiency of the proposed method for images with a noisy background.