Image Quasi Dense Matching And Co-segmentation

Recently, as techniques rapidly develop, images become the dominant informationcarriage of people. Compared with digits and texts, the content of images is much richer,which is more objective with high semantic level. In other words, images reflect scenes,objects and relationships of the objects. As for computers, image data is just in a specificway of organization. As a result, the problem of how to understand the image data isone of the key issues for machines to be intelligent. That is why image understandingbecomes one of the most fundamental and important topics in the fields of computervision and pattern recognition.To understand what images represent, representative information usually is extract-ed from pixels, and then the description is employed to organize such information. Next,the high level information is explored via finding correspondence between images. Thisprocedure is very similar with the way that human beings perceive from real world. Im-age features can be grouped into two categories, i.e. Global Image Feature and LocalImage Feature. The former one focuses on the whole image, the advantage of which isits efciency. But, it is very sensitive to image transformation, noise and occlusion. Incontrary, local image features take care of local characteristic of images. It is relative-ly robust to the factors including image transformation, noise and occlusion with longercomputational time. Fortunately, due to the development of hardware and the demand oftasks in real world, local image features have attracted more attention from researcher-s. This dissertation starts from image visual information, and focuses on image featuredescription, scene-level quasi dense matching, object-level (quasi dense) matching andobject co-segmentation.1) Mirror Reflection Invariant Description Method. Although many image fea-ture descriptors have been developed by researchers which can efectively handle scale,rotation and view-point changes, the mirror reflection remains difcult and limited work iscarried out for addressing the difculty. In this work, we propose a framework for descrip-tors to be mirror reflection invariant, which enriches most of the existing descriptors withmirror reflection invariance meanwhile preserves the original advantages. The descriptorswith more invariances broaden the applicable range of image feature descriptors. 2) Geometric Constraint Based Image Feature Matching Method. In addition,the matching of image feature descriptors is another key issue of image understanding.The performance of image feature matching is measured by two metrics, including thenumber of correct matches and the matching accuracy. According to diferent require-ments of applications, traditional matching methods usually improve one aspect by sacri-ficing the other, which limits the improvement of performance for both image understand-ing itself and its applications. This dissertation proposes a matching method enforcing ageometric constraint, i.e. Triangle Constraint, to simultaneously improve both the num-ber of correct matches and the matching accuracy and thus obtain precise and quasi densematching results.3) Object-level Matching and Co-segmentation. Based on the matching result, wefurther explore the object-level relationship within images. The exploration utilizes thescale, rotation, relative position and descriptor similarity information of matched featurepairs, without any prior knowledge, to distinguish diferent objects. Due to the charac-teristic of point-based image features, it is very unlikely to recover the whole objects byonly using the matching pairs. To recover and extract the object information as much aspossible, we finally design a co-segmentation scheme.Extensive experiments on both simulated data and real data demonstrate the efec-tiveness and robustness of our proposed methods quantitatively and qualitatively. Fromthe results, we can find that the methods proposed in this work have better performancecompared with the state-of-the-arts.