Abstract book (pdf) - ICPR 2010

ally optimal feature sets. The new approach is based on solving a mixed 0-1 linear programming problem (M01LP) by
using the branch-and-bound algorithm. In this M01LP problem, the number of constraints and variables is linear ($O(n)$)
in the number $n$ of full set features. In order to evaluate the quality of our GeFS measure, we chose the design of an intrusion
detection system (IDS) as a possible application. Experimental results obtained over the KDD Cup’99 test data set
for IDS show that the GeFS measure removes 93% of irrelevant and redundant features from the original data set, while
keeping or yielding an even better classification accuracy.
16:40-17:00, Paper TuCT2.4
Discriminative Basis Selection using Non-Negative Matrix Factorization
Jammalamadaka, Aruna, Univ. of California, Santa Barbara
Joshi, Swapna, Univ. of California, Santa Barbara
Shanmuga Vadivel, Karthikeyan, Univ. of California, Santa Barbara
Manjunath, B. S., Univ. of California, Santa Barbara
Non-negative matrix factorization (NMF) has proven to be useful in image classification applications such as face recognition.
We propose a novel discriminative basis selection method for classification of image categories based on the popular
term frequency-inverse document frequency (TF-IDF) weight used in information retrieval. We extend the algorithm to
incorporate color, and overcome the drawbacks of using unaligned images. Our method is able to choose visually significant
bases which best discriminate between categories and thus prune the classification space to increase correct classifications.
We apply our technique to ETH-80, a standard image classification benchmark dataset. Our results show that our algorithm
outperforms other state-of-the-art techniques.
17:00-17:20, Paper TuCT2.5
Recognizing Dance Motions with Segmental SVD
Deng, Liqun, Univ. of Science & Tech. of China
Leung, Howard, City Univ. of Hong Kong
Gu, Naijie, Univ. of Science & Tech. of China
Yang, Yang, Univ. of Science & Tech. of China
In this paper, a novel concept of segmental singular value decomposition (SegSVD) is proposed to represent a motion
pattern with a hierarchical structure. The similarity measure based on the SegSVD representation is also proposed. SegSVD
is capable of capturing the temporal information of the time series. It is effective in matching patterns in a time series in
which the start and end points of the patterns are not known in advance. We evaluate the performance of our method on
both isolated motion classification and continuous motion recognition for dance movements. Experiments show that our
method outperforms existing work in terms of higher recognition accuracy.
TuCT3 Marmara Hall
Object Detection and Recognition – III Regular Session
Session chair: Nixon, Mark (Univ. of Southampton)
15:40-16:00, Paper TuCT3.1
Multi-Class Graph Boosting with Subgraph Sharing for Object Recognition
Zhang, Bang, Univ. of New South Wales, National ICT Australia
Ye, Getian, Univ. of New South Wales
Wang, Yang, National ICT Australia, Univ. of New South Wales
Wang, Wei, Univ. of New South Wales
Xu, Jie, National ICT Australia, Univ. of New South Wales
Herman, Gunawan, National ICT Australia, Univ. of New South Wales
Yang, Jun, National ICT Australia, Univ. of New South Wales
In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed
method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to
solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously.
And subgragphs, which are shareable by different classes, are wisely used to improve the classification performance. The
experimental results on multi-class object recognition show the effectiveness of the proposed algorithm.
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