3. Comparisons

We have studied four on-line image retrieval systems, namely ImageMiner, Netra, Excalibur and QBIC. These systems have some common features.

In terms of query types and interface, all systems provide search on color, shape, texture or combination of them. Retrieved images are also ranked by relevance. Query by graphical exmapleall is available in all systems. However, all these systems do not support refinement search. Users cannot search images in a subset of database images. This is partcially because refinement search requires a large temporary storage for each search session and it seems impossible for Internet applications since number of search sessions is unpreditcable. Moreover, all systems do not have a facility to allow users to provide relevance feedback to the system. Especially for those textual descritpion-based image retrieval systems like ImageMiner, user relevace feedback is important for updating the image description to improve subsequent retrieval.
 
In terms of attribute representation and similarity matching, Netra, Excalibur and QBIC presents a similar scheme which is very different from ImageMiner. The former three systems use feature vector to represent the attributes. Distance-based measure is applied to similarity matching in these systems. For ImageMiner, attributes are represented in textual content descriptions and similarity matching is computed in Correlation measure.

ImageMiner, Netra, Excalibur and QBIC also have some distinct features.

In terms of attribute representation and similarity matching, Netra uses Eculidean metric to compute distance for Color and Shape attribute and adopts Mean character distance measure for Texture. Excalibur and QBIC uses Euclidean metric measure for all attributes.
 
Netra and Excalibur allow users to vary the importance of each search attribute but the way they do it is different. Netra implicitly uses the order of attributes user specifies whereas in Excalibur users can explicitly specify the weight percentage of each attribute.

In QBIC, search results are displayed in thumbnails which can be clicked to see the full-size image. It also supports search by keywords and URL. ImageMiner on-line demo does not provide text search but the vendor promises support for this functionality. QBIC also supports search by color layout and color percentage search such as "find all image regions that have 70 % blue and 20 % green".

Netra supports search by location to further disambiguate the retrievals. Netra also supports search by color percentage.

In Excalibur, users can search images by brightness, hue and saturation, and ratio of image's width to height.

ImageMiner supports conceptual query (query on objects in the image) thus semantics of the image can be quried to some degree. ImageMiner also supports search in 17 languages thus can be widely used across countries.
 
In terms of implementation, ImageMiner and Netra demos are java applet-based whereas QBIC and Excalibu uses CGI program. As we know, CGI-based client-server communication enforces browser screen refresh at each client request. But applet-based program make the page retain the same during interaction with users. Refreshing may disturb user attentions. The problem is espeically serious in QBIC system.