Computer Science – Databases
Scientific paper
2009-04-26
Computer Science
Databases
A preliminary version of this paper appeared in the Proceedings of the 1st ACM International Workshop on Multimedia Databases,
Scientific paper
The typical content-based image retrieval problem is to find images within a database that are similar to a given query image. This paper presents a solution to a different problem, namely that of content based sub-image retrieval, i.e., finding images from a database that contains another image. Note that this is different from finding a region in a (segmented) image that is similar to another image region given as a query. We present a technique for CBsIR that explores relevance feedback, i.e., the user's input on intermediary results, in order to improve retrieval efficiency. Upon modeling images as a set of overlapping and recursive tiles, we use a tile re-weighting scheme that assigns penalties to each tile of the database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant and irrelevant images identified by the user. Each tile is modeled by means of its color content using a compact but very efficient method which can, indirectly, capture some notion of texture as well, despite the fact that only color information is maintained. Performance evaluation on a largely heterogeneous dataset of over 10,000 images shows that the system can achieve a stable average recall value of 70% within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds on an off-the-shelf desktop computer.
Luo Jie
Nascimento Mario A.
No associations
LandOfFree
Content-Based Sub-Image Retrieval with Relevance Feedback does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Content-Based Sub-Image Retrieval with Relevance Feedback, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Content-Based Sub-Image Retrieval with Relevance Feedback will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-371090