Image segmentation by object contour extraction: a top down and bottom up approach combining object recognition and local cues

Computer image understanding of pictorial information has many useful applications and is one of most researched areas in computer science. The process of automated image understanding often requires that the image is first automatically segmented. Image segmentation divides an image into coherent parts and has been predominantly performed by grouping regions of similarity or by partitioning the image based on edge detection. This form of segmentation is frequently unsatisfactory because it fails to segment at meaningful object boundaries. Therefore, our work is aimed at segmenting objects by extracting (or detecting) their boundaries. The segmentation of object boundaries and their identification can be considered as the first step towards semantic image scene understanding. In this work we introduce a new image representation that combines wedgelets and discrete orthogonal polynomials to model straight edges and textures. We use an Ant Colony Optimization (ACO) algorithm to search for object contours that satisfy local constraints using local cues. The ACO algorithm performs a biased exploration in alternation with optimization allowing us to escape local maxima. However, the results show that local cues do not provide sufficient information to the Ant algorithm to enable it to construct viable object contours. To overcome the local cue limitations, we examine the use of global cues, specifically the shape of the object being segmented, but this leads us to a potential paradox. If the purpose of segmentation is to obtain the identity of the object, then how can we obtain the shape of the object without first knowing its identity? We resolve this apparent paradox by using other methods to first identify the object and then use the result of the identification to guide the segmentation. Based on this concept we have developed a segmentation algorithm which first identifies the object using image patches and then segments the object using the active contour algorithm. The algorithm is rotation, translation and scale invariant. However, using image-based cues for recognition has limitations for general categorization, as many objects are usually categorized by their shape. We solve this by developing a shape-based cue for object recognition that is biologically inspired. Shape-based cues allow us to recognize objects based on their global shape. Our experimental results show that our method is comparable or better than known methods. We then use the edge-based cues to construct the objects contour detection using the ACO algorithm developed earlier. In this sense our work combines a top-down, bottom-up segmentation approach that has parallels in human visual systems.