MULTI-FEATURE CONTENT-BASED IMAGE RETRIEVAL PUNPITI PIAMSA-NGA, NIKITAS A. ALEXANDRIDIS SANAN SRAKAEW, and GEORGE BLANKENSHIP EECS, GWU, Washington DC, 20052 USA G. PAPAKONSTANTINOU, P. TSANAKAS, and S. TZAFESTAS EECS, National Tech. Univ. of Athens, Athens Greece ABSTRACT In this paper, we propose a model for the fast retrieval image data using multiple features based on multiresolution processing. We demonstrate our performance results by searching an image database of approximately 30,000 24-bit color photographic images. We use identical structures to build the index keys from the two features using histogram generation and wavelet transform. The experiments show that the results obtained with the quad-tree structure for the two features are more accurate than using just one feature; however, the retrieval time almost doubles. The retrieval time can be improved by applying our weighted cascading comparison scheme, which allows the weighting of the features to control a pipeline. We evaluate our multiple- feature similarity-matching algorithm by comparing its accuracy with a regular-matching algorithm. The results also show that the retrieval time of our weighted cascading scheme is faster than the regular matching algorithm with an acceptable accuracy. 1. INTRODUCTION Current automatic content-based image retrieval systems use different features of interest as keys for indexing and searching of the data. [1] The number of content types that defines the identifiable features is extremely diverse. A type-dependent solution using specific features may not be appropriate in all cases. The generalized model that unifies as many types of features as possible overcomes this problem by exploiting the same computing algorithms on similar data structures. The k-tree was introduced in [2] as a unified structure for multimedia data. It allows the use of a single data structure and processing algorithm multiple types of data content. The k-tree is a directed graph where each node has 2 k incoming edges and one outgoing edge with a balanced structure. A tree expresses the hierarchical relationships of data between adjacent levels. The k-tree is used to store k-dimensional data. [3] In the case of two dimensional image data the value of k is two. The 2-k tree is known as a quad tree. Content-based image retrieval is done by comparing features extracted from the query with features extracted from every record in the database. We use a quad tree to store the features. Using the globally summarized information at the root of the tree to exclude the obvious, the algorithm gains efficiency. The more detailed rows of the tree are used when more precise definition is needed. Since we can realize all types of data, including its associated data, using the same quad tree data structure, data indexing and retrieving are uniform. Image data is composed of many features, such as color and texture. Each node on the quad-tree shares the structure to contain information of many different features. Similarity searching is a two step algorithm. The first step is to find the distance between the query and each record in the database. The second step is to sort the distances. The distance computation takes into account all of the features that were used to index the database. The best results are the database records with the lowest distances to the query. The comparison time grows with the number of features that form the basis of the distance computation. The cascading processing of the processing offers some promise for relief from the explosion of computation time. The cascading uses the ordering of one feature as the basis for the next feature. Each feature is filter for following features. We introduce a weighted cascading scheme for compromise between retrieval time and accuracy of the search. The weighted cascade uses the existing distances, which were calculated in the prior steps of the computation, to evaluation of the result, rather than using only the weight of current step. In this paper, we propose a model for the fast retrieval image data using multiple features based upon multiresolution processing. We demonstrate our performance results by the use of an image database. We use identical structures to build the two index keys of color and texture. The experiments show that inquiries using one or both features are more accurate with the quad-tree structure, while the retrieval time is only slightly longer. The retrieval time, however, can be improved by applying our weighted cascading scheme, which allows the weighting of importance of multiple features. We evaluate the multi-feature similarity matching by comparing the accuracy against a regular-