User Adaptive Clustering for Large Image Databases

Abstract--Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We compared our method with a similar approach (but without users feedback) named CLUE. Our experimental results show that by considering the user feedback, the accuracy of clustering is considerably improved. Keywords-content-based image retrieval; unsupervised learning; hierarchical cluster...

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Mohammad Mehdi Saboorian, Mansour Jamzad, Hamid R. Rabiee
• Keywords: Computer Vision, Hierarchical Clustering, ICPR 2010, Image, Large Image Database