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Image Classication for Content Based Image Retrieval
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.
User adaptive clustering for large image databases
, Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4271-4274 ; 10514651 (ISSN) ; 9780769541099 (ISBN) ; Jamzad, M ; Rabiee, H. R ; Sharif University of Technology
2010
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...