Content And Concept Indexing For High-Dimensional Multimedia Data

Abstract

Although the semantic understanding of multimedia content is immediate for humans, it is far from it for a computer. This problem is commonly called the semantic gap and is one of the major problems in multimedia retrieval. Therefore, to achieve better retrieval performance, low-level content features must be associated with semantic features effectively. In this study, we focus on the retrieval of multimedia data by combining semantic information with data content in an attempt to effectively solve the semantic gap problem. The main idea behind the combining content and concept descriptors of multimedia data is to represent the content information with the semantic information together by adding content descriptor as a new dimension to our index structure. This new dimension is constructed using a fuzzy cluster algorithm called Array Index. Thus, a new index structure which supports multimedia data querying, including fuzzy querying, is presented in this paper. The construction and query algorithms of this proposed index structure are explained throughout this paper. Experiments show that our new index structure is better than an index mechanism that stores content and concept descriptors in separate structures when the size of the data is large.