Learning similarity measure for natural image retrieval with relevance feedback

• Published in: IEEE transaction on neural networks and learning systems 2002-07, Vol.13 (4), p.811-820
• Main Authors: Guo-Dong Guo, Jain, A.K., Wei-Ying Ma, Hong-Jiang Zhang
• Format: Article
• Language: English
• Subjects: Algorithms; Boundaries; Content based retrieval; Euclidean distance; Feedback; Humans; Image databases; Image retrieval; Indexing; Information retrieval; Learning; Machine learning; Retrieval; Similarity; Studies; Support vector machines
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2002.1021882

A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.