Image features play a vital role in image retrieval. This chapter presents the use of Zernike moment features for retrieving the binary and gray level images from established image databases. To retrieve a set of similar category of images from an image database, up to 25 Zernike moment features from order zero to order 8 were utilized and experimented in this chapter. A total of 1400 binary images from MPEG-7 dataset and 1440 images from a COIL-20 dataset were used to evaluate the capability of Zernike moments features for image retrieval. The experimental results show that Zernike moments implementation is suitable for image retrieval due to rotation invariance and fast computation.
Corner detection is basically a methods used to extract certain kind of features in images which could produce some information including the location or position of the corner points. Thus, in this paper an enhancement shape corner detection method is proposed to detect true corners of shape images. The overall performance of the proposed enhanced shape corner detector and six other existing shape detectors and descriptors including Harris, SUSAN, Harris-Laplace, CSS, SIFT and global and local curvature properties is presented. The experimental results of corner detection methods are tested using the benchmark binary image MPEG-7 Core Experiment Shape-1 Part B dataset. To measure the performance of corner detection evaluation, an appropriate number of true corners were determined.
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