Image Retrieval Using Random Forest-Based Semantic Similarity Measures and SURF-Based Visual Words

In this paper, we propose a novel image retrieval scheme using random forest-based semantic similarity measures and SURF-based bag of visual words. A patch-based representation for the images is carried out with SURF-based bag of visual words. A random forest, which is an ensemble of randomized decision trees, is applied next on a set of training images. The training images accumulate into different leaf nodes in each decision tree of the random forest as a result. During retrieval, a query image, represented using SURF-based bag of visual words, is passed through each decision tree. We define a query path and a semantic neighbor set for such query images in all the decision trees. Different measures of semantic image similarity are derived by exploring the characteristics of query paths and semantic neighbor sets. Experimental results on the publicly available COIL-100 image database clearly demonstrate the superior performance of the proposed content-based image retrieval (CBIR) method with these new measures over some of the similar existing approaches.