Abstract
A new method for content-based image retrieval is being presented. This method uses a vector-space model to represent images in a multidimensional space. This model allows the use of multiple attributes in the retrieval process and also identifies the most selective values for each attribute. Therefore by ignoring the less significant values the user can reduce the dimensionality of the feature set and simplify the vector model. It also allows the user to choose any similarity measure depending on the application. The user can also assign weights to the different attributes depending on the retrieval mechanism intended. These characteristics of the retrieval method increase the retrieval efficiency and makes the model very flexible as it can be used universally for retrieving images from different domains.
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