Relevance Feedback Based on Texture Histogram and SVM

For the semantic gap between the low-level similarity and the high-level user´s query in content- based image retrieval, this paper proposes a retrieval strategy comprising two aspects to remedy the semantic gap. The one is to use the texture histogram to class the images which consistent with human vision perception and the low-level feature of images. The other is to utilize both positive and negative feedbacks for image retrieval based on support vector machines (SVM). Experimental results show that the model has good effectiveness.