This paper introduces a composite relevance feedback approach for image retrieval using transaction-based and SVM-based learning. A transaction repository is dynamically constructed by applying these two learning techniques on positive and negative session-term feedback. This repository semantically relates each database image to the query images having been used to date. The query semantic feature vector can then be computed using the current feedback and the semantic values in the repository. The correlation measures the semantic similarity between the query image and each database image. Furthermore, the SVM is applied on the session-term feedback to learn the hyperplane for measuring the visual similarity between the query image and each database image. These two similarity measures are normalized and combined to return the retrieved images. Our extensive experimental results show that the proposed approach offers average retrieval precision as high as 88.59% after three iterations. Comprehensive comparisons with peer systems reveal that our system yields the highest retrieval accuracy after two iterations.