A fast compression-based similarity measure with applications to content-based image retrieval
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
We present a new lower complexity approach for content based image retrieval based on a relative compressibility similarity measure using VQ codebooks employing feature vectors based on color and position. In previous work we have developed a system that employs feature vectors that are a combination of color and position. In this paper, we present a new approach that decouples color and position. We present this approach as two methods. The first trains separate codebooks for color and position features, eliminating the need for potentially application specific feature weightings during training. The second method achieves nearly the same performance at greatly reduced complexity by partitioning images into regions and training high-rate TSVQ codebooks for each region (i.e., position information is made implicit). Features extracted from query regions are encoded with the corresponding database region codebooks. The maximum number of codewords that a database region codebook may contain is determined at runtime and is a function of the query features. Region codebooks are then pruned appropriately before encoding query features. Experiments performed on the COREL image database show this new approach to provide almost equivalent retrieval precision to our previous method of jointly trained codebooks (and an improvement over previous methods) at much lower complexity.