Image Processing using Interactive Genetic Algorithm

Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has become an important research issue. However, most of the proposed approaches emphasize on finding the best representation for different image features. Furthermore, very few of the representative works well consider the userís subjectivity and preferences in the retrieval process.

In this paper, a user-oriented mechanism for CBIR method based on an interactive genetic algorithm (IGA) is proposed. Color attributes like the mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histogram of an image is also considered as the texture features. Furthermore, to reduce the gap between the retrieval results and the userís expectation, the IGA is employed to help the users identify the images that are most satisfied to the usersí need. Experimental results and comparisons demonstrate the feasibility of the proposed approach.

Existing System:

In the existing system the CBIR method faced a lot of disadvantage in case of the image retrival. The following are the main disadvantage faced in case of the medical field Ė Medical image description is an important problem in content-based medical image retrieval. Hierarchical medical image semantic features description model is proposed according to the main sources to get semantic features currently. Hence we propose the new algorithm to overcome the existing system.

In existing system, Images were first annotated with text and then searched using a text-based approach from traditional database management systems.

Proposed System:

In case of the proposed system we use the following method to improve the efficiency. They are as follows.

We implemented our models in a CBIR system for a specific application domain, the retrieval of coats of arms. We implemented altogether 19 features, including a color histogram, symmetry features. Content-based image retrieval, uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image.

Modules:

  • RGB Projection
  • Image Utility
  • Comparable Image
  • Similarity Images
  • Result

Tools Used:

Front End : Microsoft Visual Studio .Net 2005
Coding Language : C#.Net