Automatic Determination of Feature Weights for Multi-feature CBIR

Abstract

Image and video retrieval are both currently dominated by approaches which combine the outputs of several different representations or features. The ways in which the combination can be done is an established research problem in content-based image retrieval (CBIR). These approaches vary from image clustering through to semantic frameworks and mid-level visual features to ultimately determine sets of relative weights for the non-linear combination of features. Simple approaches to determining these weights revolve around executing a standard set of queries with known relevance judgements on some form of training data and is iterative in nature. Whilst successful, this requires both training data and human intervention to derive the optimal weights.