CBIR is about developing an image search engine, not only by using the text annotated to the image by an end user (as traditional image search engines), but also using the visual contents available into the images itselves.
Initially, CBIR system should has a database, containing several images to be searched. Then, it should derive the feature vectors of these images, and stores them into a data structure like on of the “Tree Data Structures” (these structures will improve searching efficiancy).
A CBIR system gets a query from user, whether an image or the specification of the desired image. Then, it searchs the whole database in order to find the most similar images to the input or desired image.
The main issues in improving CBIR systems are:
- Which features should be derived to describe the images better within database
- Which data structure should be used to store the feature vectors
- Which learning algorithms should be used in order to make the CBIR wiser
- How to participate the user’s feedback in order to improve the searching result
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My final thesis is about improving a CBIR system by menas of learning algorithms. So I will write about it in detail here.
I am currently working on these issues:
- Color and texture feature derivation
- Image blocking (related to the previous one)
- Color Indexing