Efficient CBIR using Color Histogram Processing | A Robust CBIR System. Este artículo no está disponible.Efficient CBIR using Color Histogram Processing | A Robust CBIR System

The need for efficient content-based image retrieval has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In CBIR (Content-Based Image Retrieval), visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the query The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems.

The need for efficient content-based image retrieval has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In CBIR (Content-Based Image Retrieval), visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the query The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems.