Abstract book (pdf) - ICPR 2010

Most of existing dimensionality reduction methods obtain the low-dimensional embedding via preserving a certain property
of the data, such as locality, neighborhood relationship. However, the intrinsic cluster structure of data, which plays a key
role in analyzing and utilizing the data, has been ignored by the state-of-the-art dimensionality reduction methods. Hence,
in this paper we propose a novel dimensionality reduction method called Cluster Preserving Embedding(CPE), in which
the cluster structure of original data is preserved via preserving the robust path-based similarity between pairwise points.
We present two different methods to preserve this similarity. One is the Multidimensional Scaling(MDS) way, which tries
to preserve similarity matrix accurately, the other one is a Laplacian-style way, which preserves the topological partial
order of the similarity rather than similarity itself. Encouraging experimental results on a toy data set and handwritten
digits from MNIST database demonstrate the effectiveness of our Cluster Preserving Embedding method.
15:00-17:10, Paper MoBT9.30
Color Image Analysis by Quaternion Zernike Moments
Chen, Beijing, Southeast Univ.
Shu, Huazhong, Southeast Univ.
Zhang, Hui, Southeast Univ.
Chen, Gang, Southeast Univ.
Luo, Limin, Southeast Univ.
Moments and moment invariants are useful tool in pattern recognition and image analysis. Conventional methods to deal
with color images are based on RGB decomposition or graying. In this paper, by using the theory of quaternions, we introduce
a set of quaternion Zernike moments (QZMs) for color images in a holistic manner. It is shown that the QZMs
can be obtained via the conventional Zernike moments of each channel. We also construct a set of combined invariants to
rotation and translation (RT) using the modulus of central QZMs. Experimental results show that the proposed descriptors
are more efficient than the existing ones.
15:00-17:10, Paper MoBT9.32
Topic-Sensitive Tag Ranking
Jin, Yan’An, Huazhong Univ. of Science and Tech.
Li, Ruixuan, Huazhong Univ. of Science and Tech.
Lu, Zhengding, Huazhong Univ. of Science and Tech.
Wen, Kunmei, Huazhong Univ. of Science and Tech.
Gu, Xiwu, Huazhong Univ. of Science and Tech.
Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the
tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. In this paper,
we propose a topic-sensitive tag ranking (TSTR) approach to rate the tags on the web. We employ a generative probabilistic
model to associate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relationships
and perform a topic-level random walk over the graph to suggest a ranking score for each tag at different topics. Experimental
results validate the effectiveness of the proposed tag ranking approach.
15:00-17:10, Paper MoBT9.33
Water Reflection Detection using a Flip Invariant Shape Detector
Zhang, Hua, Tianjin Univ.
Guo, Xiaojie, Tianjin Univ.
Cao, Xiaochun, Tianjin Univ.
Water reflection detection is a tough task in computer vision, since the reflection is distorted by ripples irregularly. This
paper proposes an effective method to detect water reflections. We introduce a descriptor that is not only invariant to
scales, rotations and affine transformations, but also tolerant to the flip transformation and even non-rigid distortions, such
as ripple effects. We analyze the structure of our descriptor and show how it outperforms the existing mirror feature descriptors
in the context of water reflection. The experimental results demonstrate that our method is able to detect the
water reflections.
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