Toward integrating text and images for multimedia retrieval in heterogeneous data mining
Sumeet Dua, Vinay Mannava · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
The problem of heterogeneous data mining deals with the computational challenges of searching multimedia data in a unified computational framework that can answer similarity queries of data mining by accurate and efficient means. The advances in data collection methodologies have generated large data-warehouses, in assortment of application domains, including but not limited to, Internet applications for multimedia retrieval and exchange. Heterogeneous data indexing has proven to be a valuable tool for complex data mining in large data domains inherently semi-structured in nature. We propose a solution to integrate the feature vectors of image and text by cooperatively representing them in a multidimensional spatial data structure, which has previously exhibited superior search performance in image database domains. We have evaluated results of content-based similarity queries on the indexing schema independently in images and textual domains. We have then studied and represented the effect of the choice of similarity metric on the similarity queries. We then propose an indexing schema that integrates the feature vectors of text and images to answer integrated queries on the unified heterogeneous data space. An added advantage of the proposed methodology is embodied by the fact that a textual feature vector can query a heterogeneous database to retrieve both text as well as images as query results. This solves the problem of individually querying each data-domain separately and sequentially scanning the integrated database for similarity results. The proposed methodology is time and space efficient, and is capable of answering complex heterogeneous data mining queries in multimedia domains.