1. Introduction

The survey studies and evaluates three image retrieval systems available on the Internet. Netra, Excalibur and ImageMiner are examined. They are compared to IBM QBIC system.

Netra (W.Y. Ma, 1997) provides content-based image retrieval by color, texture, shape and spatial location. Attributes are represented in feature vectors. The system uses Euclidean difference measure to calculate difference between two attribute feature vectors. Users can search a image using multiple attributes and vary the relative importance of each attribute. Search result is ranked by relevance.

Excalibur system provides similar search functionality as Netra. Attributes are also denoted in feature vectors. It uses Shadow Play algorithm to match images.

ImageMiner (T. Hermes, et.al., 1995) provides content-based image retrieval by color, texture, and shape and objects. The system aims to support conceptual query search. Thus the attributes are represented in textual descriptions. Text matching techniques are used to compute the difference between two descriptions.

Comparing with QBIC (W. Niblack, et.al., 1993) in terms of presentation, Netra offers users more advanced query facilities e.g. drawing the interested shapes on the fly; Excalibur does not have keyword search facility; ImageMiner offers query on high-level abstraction i.e. objects in the image.. But search refinement is not available on any of these systems.

Comparing with QBIC in terms of representation, Netra and Excalibur use the similar attributes representation scheme: feature vectors. ImageMiner uses textual description.

The ideal system should require the least amount knowledge about query language from users. Refinement search and query by example should always be there. Advanced query like drawing interested shapes is preferred. At the same time, the response time is another consideration. Last but not the least, the system should provide relevance feedback mechanism so that it can learn from users about the accuracy of search.