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.
In @cite_10 a new method called HTM (Hierarchical Tree Matching) for the CBsIR problem was proposed. It has three main components: (1) a tree structure that models a hierarchical partition of images into tiles using color features, (2) an index sequence to represent the tree structure (allowing fast access during the search phase), and (3) a search strategy based on the tree structures of both database images and the query image. Since the tree structure presented in @cite_10 is re-used in our work, we detail it in the following.
@cite_10
2007872281
This paper deals with the problem of finding images that contain a given query image, the so-called content-based sub-image retrieval. We propose an approach based on a hierarchical tree that encodes the color feature of image tiles which are in turn stored as an index sequence. The index sequences of both candidate images and the query sub-image are then compared in order to rank the database images suitability with respect to the query. In our experiments, using 10,000 images and disk-resident metadata, for 60 (80) of the queries the relevant image, i.e., the one where the query sub-image was extracted from, was found among the first 10 (50) retrieved images in about 0.15 sec.