A User-Oriented Image Retrieval System Based
on Interactive Genetic Algorithm
ABSTRACT:
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 user’s 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 users’ expectation, the IGA is employed to help the users identify the images that are most satisfied to the users’ 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 - 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 over come 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
Hardware Requirements
• SYSTEM : Pentium IV 2.4 GHz
• HARD DISK : 40 GB
• FLOPPY DRIVE : 1.44 MB
• MONITOR : 15 VGA colour
• MOUSE : Logitech.
• RAM : 256 MB
• KEYBOARD : 110 keys enhanced.
Software Requirements
• Operating system :- Windows XP Professional
• Front End :- Microsoft Visual Studio .Net 2005
• Coding Language : - C# 2005.
Modules:
1) RGB Projection
2) Image Utility
3) Comparable Image
4) Similarity Images
5) Result
Module Description:
1) RGB Projections:
The RGB color model is an additive color model in which red, green, and blue light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three additive primary colors, red, green, and blue. The main purpose of the RGB color model is for the sensing, representation, and display of images in electronic systems, such as conventional photography.
In this module the RGB Projections is used to find the size of the image vertically and horizontally.
2) Image Utility:
Whenever minimizing the error of classification is interesting for CBIR, this criterion does not completely reflect the user satisfaction. Other utility criteria Closer to this, such as precision, should provide more efficient selections.
3) Comparable Image:
In this module a reselection technique to speed up the selection process, which leads to a computational complexity negligible compared to the size of the database for the whole active learning process. All these components are integrated in our retrieval system, called RETIN and the user gives new labels for images, and they are compared to the current classification. If the user mostly gives relevant labels, the system should propose new images for labeling around a higher rank to get more irrelevant labels.
4) Similarity measure:
The results in terms of mean average precision according to the training set size (we omit the KFD which gives results very close to inductive SVMs) for both ANN and Corel databases. One can see that the classification-based methods give the best results, showing the power of statistical methods over geometrical approaches, like the one reported here (similarity refinement method).
5) Result:
Finally, the image will take the relevant image what the user search. One can see that we have selected concepts of different levels of complexities. The performances go from few percentages of Mean average precision to 89%. The concepts that are the most difficult to retrieve are very small and/or have a much diversified visual content. The method which aims at minimizing the error of generalization is the less efficient active learning method. The most efficient method is the precision- oriented method.
- Graph:
This module is used to determine relationships between the two Images. The precision and recall values are measured by simulating retrieval scenario. For each simulation, an image category is randomly chosen. Next, 100 images are selected using active learning and labeled according to the chosen category. These labeled images are used to train a classifier, which returns a ranking of the database. The average precision is then computed using the ranking. These simulations are repeated 1000 times, and all values are averaged to get the Mean average precision. Next, we repeat ten times these simulations to get the mean and the standard deviation of the MAP
Input/Output:
The image will take the relevant image what the user search. one can see that we have selected concepts of different levels of complexities. The performances go from few percentages of Mean average precision to 89%. The concepts that are the most difficult to retrieve are very small and/or have a very diversified visual content
Module Diagram:
REFERENCE:
Chih-Chin Lai and Ying-Chuan Chen, “A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm”, IEEE Transactions on Instrumentation and Measurement, 2011.
