Search

This paper presents an associative technique for content-based retrieval into image archive, based on a computing paradigm called Multidimensional Holographic Associative Computing (MHAC). Unlike any prior Artificial Associative Memory (AAM), MHAC has the unique ability fo focus on any subject of pixels in the sample image and retrieve learned images based on the similarity of the visual objects. In addition, MHAC is adaptive, graciously accommodative of imprecision, efficient, parallelizable, scalable and optically realizable. Together, these excellent properties of MHAC offer a promising novel approach to a content-based search into massive image archives. The paper presents the necessary transformational steps to incorporate this new mechanism into a complete image archival and retrieval system. This is the first associative search approach for content-based retrieval in image repository. The results show that this search system is capable of retrievals by using pattern objects as small as 10-15% of the query image frame at better than 90% accuracy. This demonstrates the potential of MHAC for handing contest-based image applications far beyond the capability of current associative memories. The design, methodology and performance of this system have been illustrated in this paper through its application in managing a Medical Image Archive (MEDIA).