Abstract. In this paper a novel statistical shape feature called the edge co-occurrence matrix (ECM) is proposed for image classification and retrieval. The ECM indicates the joint probability of edge directions of two pixels at a certain displacement in an image. The ECM can be applied to various tasks since it does not require any segmentation information unlike most shape features. Comparisons are conducted between the ECM and several other feature descriptors with two defect image databases. Both the classification and retrieval performances are tested and discussed. The results show that the ECM is efficient and it provides noticeable improvement to the performance of our CBIR system.
This paper discusses two statistical shape descriptors, the Edge Co-occurrence Matrix (ECM) and the Contour Co-occurrence Matrix (CCM), and their use in surface defect classification. Experiments are run on two image databases, one containing metal surface defects and the other paper surface defects. The extraction of Haralick features from the matrices is considered. The descriptors are compared to other shape descriptors from e.g. the MPEG-7 standard. The results show that the ECM and the CCM give superior classification accuracies. 2 Shape Descriptors 2.1 Edge Co-occurrence Matrix The Edge Co-occurrence Matrix (ECM) contains second order statistics on edge features in an image. It was introduced in [2], where early results from this work
In this paper a novel statistical shape feature called the Contour Co-occurrence Matrix (CCM) is proposed for image classification and retrieval. The CCM indicates the joint probability of contour directions in a chain code representation of an object's contour. Comparisons are conducted between different versions of the CCM and several other shape descriptors from e.g. the MPEG-7 standard. Experiments are run with two defect image databases. The results show that the CCM can efficiently represent and classify the difficult, irregular shapes that different defects possess.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.