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An ordinal co-occurrence matrix framework for texture retrieval

We present a novel ordinal co-occurrence matrix framework for the purpose of content-based texture retrieval. Several particularizations of the framework will be derived and tested for retrieval purposes. Features obtained using the framework represent the occurrence frequency of certain ordinal relationships at different distances and orientations. In the ordinal co-occurrence matrix framework, the actual pixel values do not affect the features, instead, the ordinal relationships between the pixels are taken into account. Therefore, the derived features are invariant to monotonic gray-level changes in the pixel values and can thus be applied to textures which may be obtained, for example, under different illumination conditions. Described ordinal co-occurrence matrix approaches are tested and compared against other well-known ordinal and nonordinal methods.

Ordinal-measure based shape correspondence

We present a novel approach to shape similarity estimation based on distance transformation and ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation, and similarity evaluation. This approach is suitable for use in shape classification, content-based image retrieval and performance evaluation of segmentation algorithms. The two latter applications are addressed in this papers. Simulation results show that in both applications our proposed measure performs quite well in quantifying shape similarity. The scores obtained using this technique reflect well the correspondence between object contours as humans perceive it.

Texture similarity evaluation using ordinal co-occurrence

Co-occurrence matrices have been successfully used in texture analysis. However, due to noise and monotonic shifts in gray levels, traditional co-occurrence analysis may lead to erroneous results. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different distances and orientations. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional gray level co-occurrence matrices.

Block-based ordinal co-occurrence matrices for texture similarity evaluation

In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at improving robustness of the basic ordinal co-occurrence. Earlier, we have introduced two approaches for building ordinal co-occurrence matrices. One considers only the center pixel of a moving window as a seed point, compares it to its anti-causal neighbors and saves the occurrences of ordinal relations between pixels in the form of co-occurrence matrices. However, in that approach problems occur especially when considering textures with slightly varying gray levels in relatively large areas. The other method improves the robustness of the earlier method by considering also other pixels than the center pixel in the thresholded window as seed points. Main drawback of that method is the increased computational complexity. In order to avoid that, a block-based approach for building the ordinal co-occurrence matrices is introduced in this paper. Retrieval accuracy of the proposed method has shown to produce better results than existing techniques.

Evaluation of shape correspondence using ordinal measures

In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR, shape classification and performance evaluation of segmentation algorithms. The proposed technique produced encouraging results when applied on the MPEG-7 test data.