In this paper, we design and implement a concept-based image retrieval system using feature information, more specifically,
edge histogram description. The general edge histogram framework is a novel index mechanism which allows us to describe a content of images. However, there is a significant drawback in the framework that it can not accommodate a concept-based retrieval. Even if images are only conceptually related with user queries, it may be capable of proving them to be irrelevant since their features can be different each other. Our system adapts an edge histogram descriptor and includes a knowledge used for capturing concepts from images. In the knowledge base, a concept is expressed as some of templates, which can be described by common edge histograms for the images to represent the concept well. The templates can be generated by
clustering the training images related with a concept. Consequently, since an image can also be matched with some of the templates, our system is able to support an automatic mechanism for indexing the image with the concept. The indexing mechanism enables users to retrieve the images related with a query which is formulated with their intended concepts. In addition, we also demonstrate that our concept-based approach makes a favorable comparison with an approach based on color or edge features.