The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%.