An efficient indexing structure for content based multimedia retrieval with relevance feedback

• Authors: Jongho Nang;Joohyoun Park
• Venue: Proceedings of the 2007 ACM symposium on Applied computing
• Year: 2007

This paper proposes an efficient indexing structure for CBMR (Content-Based Multimedia Retrieval), called HBI (Hierarchical Bitmap Index), in which each object is represented as a bitmap of size 2 · d · l bits, where d is the number of dimensions of object's feature vector and l is the number of bitmaps. In this bitmap representation, the feature (or attribute) value of object at each dimension is represented with a set of two bits each of which indicates whether it is relatively high ('11'), low ('00'), or neither ('01') compared to the feature values of other objects at a hierarchical organized interval. Using these compact representations of feature vectors, a lot of irrelevant objects could be quickly filtered-out by a couple of simple XOR operations, and it helps to reduce the filtering process of similarity search in high-dimensional data space. It also presents an optimization algorithm, called FQD (Filtering by Query Difference), for the similarity search with relevance feedback that reuses the previously calculated distances between the original query and all objects in the database when filtering the irrelevant objects in the successive search with modified query. It helps to further reduce the search time of CBMR with relevance feedback. Experimental results show that the similarity search using HBI is about 2 ~ 3 times faster than VA-File while guaranteeing the exact solutions, and FQD for relevance feedback helps to further reduce the elapsed time of successive similarity search compared to the one for the first search.