for multimedia collection browsing

For the music database retrieval, the researchers extract the features, such as melodies, rhythms and chords, from the music data and develop indices to support the quick music search. Several reports have pointed out that these features can be transformed and represented in the forms of music feature strings or numeric values such that string indexing or numeric indexing is created, respectively, for music retrieval. Recently, A real-valued feature indexing technique has been proposed for music data retrieval. This approach has demonstrated that it can support music searching more efficiently than existing schemes. However, there is only exactly matching for music retrieval discussed. The approximate searching for music database has not received many attentions yet. In this paper, we will propose an approximate searching scheme for music data retrieval in a real-valued feature index. The experimental results show that our approach outperforms existing scheme for music approximate searching.

This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps. Index terms – Content-based image retrieval, relevance feedback, region correspondence, generalized adjacency matrix, inexact graph matching, maximum likelihood estimation I.