Content-Based Image Retrieval Optimization
Abstract A new method for contentbased searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. Content-Based Image Retrieval Optimization. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system learning we applied differential evolution. This last step is very important, and significantly improves the results. The presented algorithm can be easily modified, by changing its components (feature extractor or clustering algorithm).