A parallel architecture for feature extraction in content-based image retrieval system

Although it is possible to retrieve images from database using a unique identification defined by a human operator as an index to images, it is more convenient and natural to search images based on their contents. The principle of content-based image retrieval (CBIR) system is to retrieve images based on the content of the images. One of the important components in CBIR system is to extract the visual features of the images for performing more abstract analysis. However, some of these features are computationally expensive. To solve this issue, a flexible parallel architecture has been proposed to improve the extraction time for the system. This architecture provides the software system with the flexibility of adding and removing any visual features from the system. Thus, a system becomes more intelligent and so it is able to adapt changes caused by the replacement of more appropriate visual features for representing the images.