A System for Historic Document Image Indexing and Retrieval Based on XML Database Conforming to MPEG7 Standard Wafa Maghrebi * , Anis Borchani * , Mohamed A. Khabou + , Adel M. Alimi * * REsearch Group on Intelligent Machines (REGIM) University of Sfax ENIS, DGE, BP. W-3038, Sfax, Tunisia tn . rnu . fsegs @ maghrebi . wafa , fr . yahoo @ anisborchani , org . ieee @ alimi . adel + Electrical and Computer Engineering Dept University of West Florida 11000 University Parkway, Pensacola, FL 32514, USA mkhabou@uwf.edu Abstract We present a novel image indexing and retrieval system based on object contour description. Extended curvature scale space (CSS) descriptors composed of both local and global features are used to represent and index concave and convex object shapes. These features are size, rotation, and translation invariant. The index is saved into an XML database conforming to the MPEG7 standard. Our system contains a graphical user interface that allows a user to search a database using either sample or user-drawn shapes. The system was tested using two image databases: the Tunisian National Library (TNL) database containing 430 color and gray-scale images of historic documents, mosaics, and artifacts; and the Squid dataset containing 1100 contour images of fish. Recall and precision rates of 94% and 87%, respectively, were achieved on the TNL database and 71% and 86% on the Squid database. Average response time to a query is about 2.55 sec on a 2.66 GHz Pentium-based computer with 256 Mbyte of RAM. Keywords: Image indexing, image retrieval, eccentricity, circularity, curvature space descriptors, MPEG7 standard, XML database. 1. Introduction Many image content retrieval systems were lately developed, tested, and some even made available online (e.g. Beretti [1, 2], QBIC [3], FourEyes [4], and Vindx [5, 6]). These systems use image content-based indexing methods to represent images. Image content can be represented using global features, local features, and/or by segmenting the images into “coherent” regions based on some similarity measure(s). For example, the QBIC system [3] uses global features such as texture and color to index images. Global features have some limitations in modeling perceptual aspects of shapes and usually perform poorly in the computation of similarity with partially occluded shapes. The FourEyes system [4] uses regional features to index an input image: An image is first divided into small and equal square parts then, shape, texture and other local features are extracted from these squares. These local features are then used to index the whole image. The system developed by Berretti et al [1, 2] indexes objects in an input image based on their shape and offers the user the possibility of drawing a query to retrieve all images in the database that are similar to the drawn query. The Vindx system [5, 6] uses a database of 17th century paintings. The images are manually indexed based on the shapes they contain. This method is accurate but very time consuming especially when dealing with a huge data base of images. Similar to the system developed by Berretti et al, the Vindx system also offers the user the possibility of drawing a query to retrieve all images in the database that match the query to a certain degree. Among all image indexing methods described in literature, only two methods conform to the MPEG7 standards of image contour indexing: the Zernike moment (ZM) descriptors [7] and the curvature scale space (CSS) descriptors. A good descriptor should be invariant to scale, translation, rotation, and affine transformation and should also be robust and tolerant of noise. The ZM descriptors are scale, translation and rotation invariant. However, they have the disadvantage of losing the important perceptual meaning of an