Recently with the rapid growth of multimedia industry, images are used by many people for a variety of applications. To represent and retrieve these images efficiently, various methods have been proposed. In this paper, A new method for image retrieval is presented. This method uses multiple descriptions at different levels to represent images. This model allows the use of all levels of descriptions in the retrieval process, hence permits not only semantic search but also syntactic search. The quick content-based visual access to the stored images is essential for efficient navigation through image collections. At the time of visual access to image database, the users have very different objectives. Users' interests may vary from one person to another. In order to satisfy the information needs of users, it is of paramount importance to effectively and efficiently adapt the retrieval process to each user. In our approach the retrieval process is guided by the user's retrieval intentions according to his/her needs and preferences. These characteristics of the retrieval method increase the retrieval efficiency and makes the model very flexible as it can be used universally for retrieving images from different domains. We have applied the proposed approach to a range of color images, and obtained positive results.
Retrieving images form large collections using image content is an important problem, in this multimedia age. A quick content-based visual access to the stored image is capital for efficient navigation through image collections. In this paper we introduce several techniques which characterize color homogeneous object and their spatial relationships for efficient content-based image retrieval. We present a region growing technique for efficient color homogeneous objects segmentation and extend the 2D string to an accurate description of spatial information and relationships. In order to improve content-based image retrieval, our method emphasized several objectives, such as: automated extraction of localize coherent regions and visual features, development of techniques for fast indexing and retrieval, and querying by both features and spatial information coupled with a symbolic level of image representation. We present our flexible image retrieval system and we give some experimental results.
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