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You searched for +publisher:"University of New South Wales" +contributor:("Ye, Getian, Computer Science & Engineering, Faculty of Engineering, UNSW"). One record found.

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University of New South Wales

1. Xu, Jie. On-line and unsupervised learning for codebook based visual recognition.

Degree: Computer Science & Engineering, 2011, University of New South Wales

In this thesis we develop unsupervised and on-line learning algorithmsfor codebook based visual recognition tasks. First, we study the Prob-abilistic Latent Semantic Analysis (PLSA), which is one instance ofcodebook based recognition models. It has been successfully appliedto visual recognition tasks, such as image categorization, action recog-nition, etc. However it has been learned mainly in batch mode, andtherefore it cannot handle the data that arrives sequentially. We pro-pose a novel on-line learning algorithm for learning the parameters ofthe PLSA under that situation. Our contributions are two-fold: (i)an on-line learning algorithm that learns the parameters of the PLSAmodel from incoming data; (ii) a codebook adaptation algorithm thatcan capture the full characteristics of all features during the learn-ing. Experimental results demonstrate that the proposed algorithmcan handle sequentially arriving data that the batch PLSA learningcannot cope with.We then look at the Implicit Shape Model (ISM) for object detec-tion. ISM is a codebook based model in which object information isretained in codebooks. Existing ISM based methods require manuallabeling of training data. We propose an algorithm that can label thetraining data automatically. We also propose a method for identify-ing moving edges in video frames so that object hypotheses can begenerated only from the moving edges. We compare the proposed al-gorithm with a background subtraction based moving object detectionalgorithm. The experimental results demonstrate that the proposedalgorithm achieves comparable performance to the background sub-traction based counterpart, and it even outperforms the counterpartin complex situations.We then extend the aforementioned batch algorithm for on-line learn-ing. We propose an on-line training data collection algorithm and alsoan on-line codebook based object detector. We evaluate the algorithmon three video datasets. The experimental results demonstrate thatour algorithm outperforms the state-of-the-art on-line conservativelearning algorithm. Advisors/Committee Members: Wang, Yang, Computer Science & Engineering, Faculty of Engineering, UNSW, Wang, Wei, Computer Science & Engineering, Faculty of Engineering, UNSW, Ye, Getian, Computer Science & Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Visual recognition; Online learning; Unsupervised learning

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APA (6th Edition):

Xu, J. (2011). On-line and unsupervised learning for codebook based visual recognition. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Xu, Jie. “On-line and unsupervised learning for codebook based visual recognition.” 2011. Doctoral Dissertation, University of New South Wales. Accessed October 14, 2019. http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true.

MLA Handbook (7th Edition):

Xu, Jie. “On-line and unsupervised learning for codebook based visual recognition.” 2011. Web. 14 Oct 2019.

Vancouver:

Xu J. On-line and unsupervised learning for codebook based visual recognition. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2019 Oct 14]. Available from: http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true.

Council of Science Editors:

Xu J. On-line and unsupervised learning for codebook based visual recognition. [Doctoral Dissertation]. University of New South Wales; 2011. Available from: http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true

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