Content-based Image Retrieval Components

As shown in the following figure, some major components of a CBIR are:

1. Image Database
2. Feature Extraction Block
3. Indexing Block
4. Feature Database
5. Search and Retrieval Block
6. User Interface
7. User Relevant Feedback processing Block

(From I-Search Tutorial)

 

Row Images are stored into Image database. In order to fast access to these images, some descriptors should be extracted from them, which describe them in the best way. These descriptor appear into integer or real values, in order to be comparable. These values called Feature Vectors.

These vectors make it easy to classify images into some predefined classes by classification methods, or into non-predefined clusters by clustering methods. This is the duty of a block called indexing block.

Now, system is ready to accept the queries entered by user. This query appears as an input image, which is desired image for user. Actually user tells system that retrieve some images which is most similar to the quety image.

Search and retrieval Block uses send the query image to Feature Extraction block to extract its feature vector. Then uses it to search into classes/clusters to find out which kernel of these classes/clusters is nearest to the feature vector. Then some of most similar images to the query image retriev and show to user.

After these steps, user can see the retrieved images. Some systems give this opportunity to user to select the images which satisfy his/her more than others. Then this knowledge processes and affect the previus search result so that the result may be most satisfiable for user.

Some flowchart will be added soon.

View An Image retrieval system prototype in PERSIAN/Farsi /فارسی LANGUAGE. References section will be useful for all other languages, note that all of them are available for free. All of them are listed below.

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[24] S. S. a. P. J. Hiremath P.S., “Wavelet Based Feature for Color Texture Classification with application to CBIR,” Intl. Journal of Computer Science and Network Security (IJCSNS), vol. 6, Sep. 2006.

[25] Shi Y. and Liu Y., “Binary Tree-based Clustering Algorithm and Used in Color Image Segmentation,” in 4th Intl. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD), 2007, pp. 219-223.

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