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Human action categorization using discriminative local spatio-temporal feature weighting
, Article Intelligent Data Analysis ; Volume 16, Issue 4 , July , 2012 , Pages 537-550 ; 1088467X (ISSN) ; Kasaei, S ; Sharif University of Technology
IOP
2012
Abstract
New methods based on local spatio-temporal features have exhibited significant performance in action recognition. In these methods, feature selection plays an important role to achieve a superior performance. Actions are represented by local spatio-temporal features extracted from action videos. Action representations are then classified by applying a classifier (such as k-nearest neighbor or SVM). In this paper, we have proposed two feature weighting methods to better discriminate similar actions. We have proposed a definition of feature discrimination power to be used in the feature selection process. Our proposed weighting schemes have greatly improved the final categorization accuracy on...
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...
Supervised fuzzy partitioning
, Article Pattern Recognition ; Volume 97 , 2020 ; Nateghi Haredasht, F ; Beigy, H ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features,...