4.  Conclusion

This survey studies and evaluates four image retrieval systems on Internet. They are QBIC, ImageMiner, Netra and Excalibur. Image retrieval on these systems are content-based and similarity-based. Common content attributes including color, texture and shape are supported by all attributes.  The survey examines attribute representation, similarity matching formular in each system. The survey also study the query type and presentation interface offered by these systems. These systems are compared with each other to illustrate the state-of-art in current image retrieval systems on Internet.

Basd on these studies, I think an idea system should provide, or in other words, future image retrieval system developement should consider, the following features:

Query Based on Spatial Relationship

Current systems have focused on the representation of individual image regions and search strategies for single region queries. The next immediate goal should be integrating spatial relationship between regions into the image retrieval system. ImageMiner seems to provide this functionality but it assumes that the individual objects can be matched exactly and con-
centrate only on the matching of their spatial relationships. This is usually not the case for most image database applications where each region is represented by a set of image features and exact match between features is not a reasonable assumption.  An ideal system should integrate the region features and their spatial relationship into a unified representation. Such a strategy could support for high-level object based queries.

User's Relevance Feedback for Query Refinement

Modifying the retrieval results based on user feedback is another interesting consideration. An image retrieval system should be able to incorporate relevance feedback from users. One idea (W. Y. Ma, 1997) is to utilize a nonlinear mapping to learn the appropriate transformation from the original feature space to the new space where visually similar patterns will cluster together. This mapping function can be designed such that it can be dynamically adjusted or refined based on the user relevance feedback to improve retrieval performance.

Visual Thesaurus

Combine various low-level image attributes (color, texture, shape, spatial relationship, etc.) to construct a
visual thesaurus which can index image database based on the co-occurrence properties
among these image attributes.

Besides these considerations, other factors that make an image retrieval system perfect could be:

•     multilingual keyword searech;
•     permitting users to sketch an object that they want to retrieve; and
•     allowing users to provide their own thumbnail images that they want to match against database images.