Exploiting angular profiles signature for shape-based image classification and retrieval

Image classification and retrieval has significant importance in a wide variety of applications like object recognition, tracking, and content based retrieval, etc. Images usually consist of various objects which are segmented and then analysed for object-based classification and recognition. Owing to the absence of intensity and colour information, binary objects are difficult to recognise. They are usually represented using compact, geometrically invariant and robust features extracted from the object’s contour or interior region. These features form the basis for recognition and govern the overall performance of classification systems. A new shape signature is introduced in this paper for representing shapes through angular profiles signature which are extracted from objects enclosed within minimum bounding circles. We have evaluated the discriminatory capabilities of this signature in shape recognition and retrieval on two shape datasets. Experimental results indicate that the signature is able to represent shapes effectively, achieving overall accuracy of 94%.