A Volume Image Foreground Identification Method by Dual Multi-Scale Morphological Operations

A simple and regular foreground identification (FGID) method is proposed for image indexing. The gray-level Morphological open/close by reconstruction (MOR/MCR) is operated on one image in a dual and multi-scale approach to construct a background gray-level mesh to distinguish foregrounds (FGs). The highly regular MOR/MCR operations make it feasible to deal with FG segmentation of volume images. The FGID efficiency is verified by the image retrieval performance, i.e., recall, precision and rank. With precisely identified FGs, MPEG-7 shape descriptors, in additional to color ones, can be used to improve the image retrieval performance. For the retrieval unit, a greedy boosting retrieval method is used to perform shape-based multi-instance query in considering the feature element dependency. To perform multi-instance query with multiple features, the retrieval unit integrates the similarity ranks of different feature types according to the feature saliency among query samples to yield the final similarity rank. The normalized correlation coefficient of features among query samples is computed to provide weighting factors for integrating ranks. Experiments show that the FGID unit helps much in improving the retrieval performances, i.e., 7% improvement for the precision-recall (PR) and 20% improvement for the averaged normalized modified retrieval rank (ANMRR), as compared to non-FGID ones.