Combination of classification and regression in decision tree for multi-labeling image annotation and retrieval

Abstract

This paper proposes a semantic-based image retrieval approach which refers to the ability of using keywords for searching within image datasets. This is possible by adding some textual metadata, called image annotation. Combination of classification and regression in decision tree (DT) has been employed for multi-labeling image annotation in which, more than one label will be considered for every single tuple. In the proposed approach, all concepts and their corresponding ranks will be stored in each DT leaf node instead of storing only a concept or a rank. We have used a hierarchical network of semantics to achieve a better performance. The main idea behind our approach is that in each leaf node, the system should give a higher rank to concepts with highest degree of purity and details according to prepared hierarchical semantic network. A segmented, feature extracted and annotated image dataset, SAIAPR-TC12, has been used for evaluation. A hierarchy of 256 semantic concepts which have been used in annotation process, made it very suitable for testing the approach. Experimental results confirmed that our approach illustrates better performance in comparison with single-labeling approaches which only assign one class to every single tuple and only support linear relationship among concepts.