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Simple and fast feature extraction methods are in need today for Content Based Image Retrieval (CBIR) and object recognition applications. The work presented in this paper is contour based one dimensional shape feature extraction technique for closed contour objects. The continuous contour is normalized into 'N' representative points. The sector area based object area normalization (OAN) technique is used for contour normalization. The centroid distance from all normalized points forms 1-D Compact Centroid Distance (CCD) feature vector. The experiments are conducted in MPEG-7 CE Shape-1 Part-B dataset images to test affine invariance property and image retrieval accuracy. Experimental results show the effectiveness of the proposed method in content-based image retrieval tasks.

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This paper describes a multi-scale feature integration framework using angular pattern (AP), binary AP (BAP) and sequential backward selection (SBS) algorithms. These angular descriptors are represented by multi-scale features from which the best subsets of the scales are chosen using five-fold cross-validation technique along with SBS algorithm for efficient image retrieval. The SBS algorithm reduces the dimensionality of feature space which in turn reduces the matching time complexity. The extracted AP and BAP features are represented in histograms and are compared by the Chi-square distance metric. The experimental analysis is performed on the MPEG-7 CE-1 Part-B dataset images to demonstrate the effectiveness of multi-scale feature integration using SBS algorithm. The image retrieval performance of this framework is compared with state-of-the-art shape descriptors. Being multi-scale global shape descriptors, the proposed framework captures complete information about the shape and are invariant to scaling and rotation transformations.

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