Abstract
Abstract Object recognition and segmentation are two of the most difficult and challenging problems in computer vision. Many approaches have been proposed to solve these problems. However, these two problems are usually addressed independently in previous approaches. We implement an interactive object segmentation and recognition system to solve these two problems at the same time. The main idea of this thesis comes from jigsaws. First, we over-segment every input image into many superpixels. Then we classify all superpixels based on the bag-of-words model. Finally, we combine the superpixels that belong to the same labels to obtain segmented objects. After the classification stage, there are still some misclassified superpixels. Based on the information derived from user scribbles, we can refine the misclassified superpixels interactively. The information we used in the refinement process is the spatial and co-occurrence relations between objects of different categories. Our object segmentation system favors texture objects, such as grass, tree, sky, and so on. Images with a significant subject can also have good labeling accuracy. The experimental results show that the refinement process indeed helps to improve the preliminary segmentation results.