A Content-based Image Retrieval System Based On Convex Hull Geometry

Developments in data storage technologies and image acquisition methods have led to the assemblage of large data banks. Management of these large chunks of data in an efficient manner is a challenge. Content-based Image Retrieval (CBIR) has emerged as a solution to tackle this problem. CBIR extracts images that match the query image from large image databases, based on the content. In this paper, a novel approach of comparing the convex hull geometry of the query image to that of the database image in terms of a relative metric which is denoted as the Convex Hull Area Ratio (CHAR) is used. The metric CHAR is the ratio of the area of the intersection of the two convex hulls to the area of their union. Convex hull shape polygon is extracted from the database images and the coordinate values are stored in the feature library. When a query image is given, the convex hull values are extracted in the same fashion. Ratio of the intersected area to union area of the two convex hulls (CHAR) are found and stored in an array. Subsequently, similarity measurement is performed and the maximum value of the CHAR indicates the closest match. Thus, the database images that are relevant to the given query image are retrieved. Scale and translational invariance have been preserved by a suitable co-ordinate transformation. The proposed CBIR technique is evaluated by querying different images and the retrieval efficiency is evaluated by determining precision-recall values for the retrieval results.