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This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors.

Machine learning methods have demonstrated promising performance for Content Based Image Retrieval (CBIR) using Relevance Feedback (RF). However, a very limited number of feedback images can significantly degrade the performance of these techniques. In this work, each image is represented by a vector of multiple distance measures corresponding to multiple features. Each feature is considered a sub-query for RF process. In RF process, we propose to use Pareto method to get Pareto points (also called trade-off points) according to different depths. These points are used as relevant queries for the next RF round. Experimental results show that our proposed approach is very effective to improve the performance of the classification engine.