From Logo to Object Segmentation
This paper proposes a method to segment object from the web images using logo detection. The method consists of three steps. In the first step, the logos are located from the original images by SIFT matching. Based on the logo location and the object shape model, the second step extracts the object boundary from the image. In the third step, we use the object boundary to model the object appearance, which is then used in the MRF based segmentation method to finally achieve the object segmentation. The key of our method is the object boundary extraction, which is achieved by searching a variation of the shape model that best fits the local edge of the image. Affine transform is used to consider the variations among the objects. Meanwhile, the Nelder-Mead simplex method with a simple initial rough search is used to run the boundary search. To verify the proposed method, we collect a LogoSeg dataset from the web such as Flickr and Google. The MOMI dataset is also used for the verification. The experimental results demonstrate that the proposed logo detection based segmentation method can improve the performance of the object segmentation.