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Multi-criteria Multi-class Traffic Assignment Using Mixed Generalized Extreme Value Models
, M.Sc. Thesis Sharif University of Technology ; Poorzahedy, Hossain (Supervisor)
Abstract
Relaxing the underlying assumptions of transportation networks analysis and approaching to more realistic behavioral assumptions in route choice modeling, allows more precise predictions for equilibrium traffic pattern. Furthermore, enhancing the degree of sensitivity in modeling traffic equilibrium is crucial from planning perspective in the sense that it can make transportation analyst capable to evaluate and predict the impacts of a broad spectrum of supply and demand management policies. This research has concentrated on proposing some generalizations in route choice behavior modeling and relaxing some restrictive assumptions of the previous approaches in this area. Formally speaking, in...
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...