Traditional Content-based image retrieval supports image searches based on color, texture and shape. However it is difficult and nonintuitive for most user to use those low level features to query images. And for most user they like search by keywords . For example , recently Google provide services in image search. Although it is named image search , but actually it is search by keywords ,not image-contents. For this reason MPEG-7 now support textual annotation standard which is MPEG-7 Multimedia Description Schemes (DSs) are metadata structures for describing and annotating audio-visual (AV) content. But manual annotation of image or video take time and expensive. we propose a system which could help us to make suitable auto-annotations.We extract the image factal features and use Diverse Density Algorithm for training models. In this way , user and system can interact in real-time . When trained models in database is growing, the system auto-annotation success rate is increasing.

Content- and semantic-based interactive mining systems describe remote sensing images by means of relevant features. Region-based retrieval systems have been proposed to capture the local properties of an image. Existing systems use computationally extensive methods to extract primitive features based on color, texture (spatial gray level dependency - SGLD matrices), and shape from the segmented homogenous region. The use of wavelet transform techniques has recently gained momentum in multimedia image archives to expedite the retrieval process. However, the current semantic-enabled framework for the geospatial data uses computationally extensive methods for feature extraction and image segmentation. Hence, this dissertation presents the use of a wavelet-based feature extraction in a semantics-enabled framework to expedite the knowledge discovery in geospatial data archives. Geospatial data has different characteristics than multimedia images and poses more challenges. The experimental assumptions, such as the selection of the wavelet decomposition level and mother wavelet used for multimedia data archives, might not prove to be efficient for the retrieval of geospatial data. Discrete wavelet transforms (DWT) introduce aliasing effects due to subband decimation at a certain decomposition level. This dissertation addresses the issue of selecting a suitable wavelet decomposition level, and a systematic selection process is developed for image segmentation. To validate the applicability of this method, a synthetic image is generated to assess the performance qualitatively and quantitatively. In addition, results for a Landsat7 ETM+ imagery archive are illustrated, and the F-measure is used to assess the feasibility of this method for retrieval of different classes. This dissertation also introduces a new feature set obtained by coalescing wavelet and independent component analysis for image information mining. Feature-level fusion is performed to include the missing high detail information from the panchromatic image. Results show that the presented feature set is computationally less expensive and more efficient in capturing the spectral and spatial texture information when compared to traditional approaches. After extensive experimentation with different types of mother wavelets, it can be concluded that reverse Biorthogonal wavelets of shorter length and the simple Haar filter provided better results for the image information mining from the database used in this study.