II. BACKGROUND
A. CBIR Systems
Content-based image retrieval system (CBIR) provides different ways of searching the database, enabling searches based on features such as texture, shape, and color. Such feature-based searches can be combined with searches for textual information. The CBIR systems are based on a similarity search that ranks the images in the database based on a computable measure for their similarity to a chosen image. Similarity searches often involve user interaction, whereby the user provides feedback on the relevance of the search results by selecting a different feature, or modifying the weight of certain features. Fig. 1 shows a typical architecture of a content-based image retrieval system. Two main functionalities (which take a time-consuming task) are supported: data insertion and query processing. The data insertion subsystem is responsible for extracting appropriate features from images and storing them into the image database (see dashed modules and arrows). This process is usually performed off-line [27]. The query processing, in turn, is organized as follows: the interface allows a user to specify a query by means of a query pattern and to visualize the retrieved similar images. The query-processing module extracts a feature vector from a query pattern and applies a metric (such as the Euclidean distance) to evaluate the similarity between the query image and the database images. Next, it ranks the database images in a decreasing order of similarity to the query image and forwards the most similar images to the interface module.