An Effective Mechanism to Neutralize the Semantic Gap in Content Based Image Retrieval (CBIR)

Nowadays, Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features (such as shape, color etc, ...) or high level features (human perception). Normally, a semantic gap exists between the low level features and the high level features, because the images which are identical in visual content may not be identical in the semantic sense. In this paper, an effective approach is proposed to trim down this semantic gap that exists between the low level features and the high level features. Initially, when a query image is given, images relevant to it are retrieved from the image database based on its low level features. We have performed retrieval utilizing one of the evolutionary algorithms called Evolutionary Programming (EP). Subsequent to this process, query keyword which is a high level feature is extracted from these retrieved images and then based on this query keyword, relevant images are retrieved from the database. Subsequently, the images retrieved based on low level features and high level features are compared and the images which are both visually and semantically identical are identified. Better results obtained by the proposed approach when it is queried using different types of images prove its effectiveness in minimizing the semantic gap.