Content-based image retrieval system with most relevant features among wavelet and color features
This work addresses image retrieval accuracy for digital data management, but it is incremental as it builds on existing CBIR methods with optimization.
The paper tackled the problem of sensitivity in content-based image retrieval (CBIR) by proposing a feature extraction schema combining wavelet and color features, with ant colony optimization for feature selection to maximize F-measure; results showed higher precision and recall compared to three older systems on the Corel database.
Content-based image retrieval (CBIR) has become one of the most important research directions in the domain of digital data management. In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transformation and color features in RGB and HSV domains are proposed as representative feature vector for images in database followed by appropriate similarity measure for each feature type. In CBIR systems, retrieving results are so sensitive to image features. We address this problem with selection of most relevant features among complete feature set by ant colony optimization (ACO)-based feature selection which minimize the number of features as well as maximize F-measure in CBIR system. To evaluate the performance of our proposed CBIR system, it has been compared with three older proposed systems. Results show that the precision and recall of our proposed system are higher than older ones for the majority of image categories in Corel database.