Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT8.30
User Adaptive Clustering of a Large Image Database
Saboorian, Mohammad Mehdi, Sharif Univ. of Tech.
Jamzad, Mansour, Sharif Univ. of Tech.
Rabiee, Hamid Reza, Sharif Univ. of Tech.
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 compared
our method with a similar approach (but without users feedback) named CLUE. Our experimental results show that by
considering the user feedback, the accuracy of clustering is considerably improved.
13:30-16:30, Paper ThBCT8.31
Alignment-Based Similarity of People Trajectories using Semi-Directional Statistics
Calderara, Simone, Univ. of Modena and Reggio Emilia
Prati, Andrea, Univ. of Modena and Reggio Emilia
Cucchiara, Rita, Univ. of Modena and Reggio Emilia
This paper presents a method for comparing people trajectories for video surveillance applications, based on semi-directional
statistics. In fact, the modelling of a trajectory as a sequence of angles, speeds and time lags, requires the use of a
statistical tool capable to jointly consider periodic and linear variables. Our statistical method is compared with two stateof-the-art
methods.
13:30-16:30, Paper ThBCT8.32
Contact Lens Detection based on Weighted LBP
Zhang, Hui, Shanghai Inst. of Tech.
Sun, Zhenan, Chinese Acad. of Sciences
Tan, Tieniu, Chinese Acad. of Sciences
Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged.
Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper,
we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified
SIFT descriptor is extracted at each pixel of the image. Secondly, the SIFT descriptor is used to rank the LBP encoding
sequence. Then, statistical features are extracted from the weighted LBP map. Lastly, SVM classifier is employed to
classify the genuine and counterfeit iris images. Extensive experiments are conducted on a database containing more than
5000 fake iris images by wearing 70 kinds of contact lens, and captured by four iris devices. Experimental results show
that the proposed method achieves state-of-the-art performance in contact lens spoof detection.
13:30-16:30, Paper ThBCT8.33
Integrating ILSR to Bag-of-Visual Words Model based on Sparse Codes of SIFT Features Representations
Wu, Lina, Univ. Beijing
Luo, Siwei, Univ. Beijing
Sun, Wei, Beijing Jiaotong Univ.
Zheng, Xiang, Beijing Jiaotong Univ.
In computer vision, the bag-of-visual words(BOV) approach has been shown to yield state-of-the-art results. To improve
BOV model, we use sparse codes of SIFT features instead of previous vector quantization (VQ) such as k-means, due to
more quantization errors of VQ. And as local features in most categories have spatial dependence in real world, we use
neighbor features of one local feature as its implicit local spatial relationship (ILSR). This paper proposes an object categorization
algorithm which integrate implicit local spatial relationship with its appearance features based on sparse codes
of SIFT to form two sources of information for categorization. The algorithm is applied in Caltech-101 and Caltech-256
datasets to validate its effectiveness. The experimental results show its good performance.
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