Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

Image Denoising is one of the fundamental and very important necessary processes in image processing. It is still a challenging and a hot problem for researchers. Images are one of essential representations in every field like education, agriculture, geosciences, aerospace, surveillance, entertainment etc by means of electronic or print media. Images can get corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods have been proposed. An overview of various methods is given here after a brief introduction. These methods have been categorized on the bases of techniques used.

By gaining the place of active and important research area, Content based image retrieval has been proposed in a number of different ways after its inception. In the proposed method, a new angle orientation histogram has been introduced named as Angle Edge Histogram. By applying Pythagorean theory to image, very useful characteristics have been obtained for image matching, search and retrieval. Proposed method has also been compared with existing methods and the results show that it outperforms the existing methods in values of precision and recall and balance of precision and recall. Proposed method receives an average of 94% of precision and 79% of recall rates.