Dimensionality reduction to improve content-based image retrieval: A clustering approach

Techniques of Content-Based Image Retrieval (CBIR) employ a mathematical representation of an image (also called feature vector), to characterize the image in the retrieval process. The feature vector-based representation of an image in CBIR systems causes the "semantic gap" problem, which is the inconsistency between the low-level image feature representation and the high-level image interpretation. However, the usage of a large number of features to represent an image, which seems to be a solution for the semantic gap, leads to the "dimensionality curse" problem. In this paper, we propose to amend the semantic gap along with the dimensionality curse by a dimensionality reduction method called FTK (Feature Transformation based on K-means). FTK performs feature transformation by clustering the feature vector. It employs the clustering principle of k-means to compact the feature vector space. The results indicate that clustering is an approach well-suited to perform dimensionality reduction in CBIR systems.