This paper proposes new approaches for image representation to bridge the gap between visual features and semantics. Two new combined feature extraction approaches are used to extract significant features from images. Each approach is a hybrid of two feature extraction methods and tries to capture both colour and texture information. In order to improve the query processing time and avoid the linear search problem, a clustering technique is applied on the image dataset according to each feature extraction approach. The clustering outcomes of the two feature extraction approaches are combined together using a decision fusion technique. The fused results show an improvement over any single approach. An implemented prototype system demonstrates a promising retrieval performance examined on 1000 colour images from CORAL dataset in comparison with a peer system in literature.