Abstract book (pdf) - ICPR 2010

Research on complex shape recognition showed that the shape context algorithm is sensitive to relative position variation
of articulation. Aimed at this problem, a shape recognition method is proposed based on local shape filling rate of various
object silhouettes. We take each landmark point as a circle center and use as its radius. Then, under a particular radius, the
ratio between the covered silhouette pixels and the total pixels is defined as local shape filling rate. Thus, different radius
may form different local shape filling rates. All landmark points with different radius will constitute a characteristic matrix
which can effectively reflects the entire statistical property of the object shape. Experiments on a variety of shape databases
show that the novel method is insensitive to articulation and less influenced by the number of landmark points, so our algorithm
has strong power in describing object details.
15:00-17:10, Paper MoBT9.2
Learning Gmm using Elliptically Contoured Distributions
Li, Bo, Beijing Inst. of Tech.
Liu, Wenju, Chinese Acad. of Sciences
Dou, Lihua, Beijing Inst. of Tech.
Model order selection and parameter estimation for Gaussian mixture model (GMM) are important issues for clustering
analysis and density estimation. Most methods for model selection usually add a penalty term in the objective function
that can penalize the models and choose an optimal one from a set of candidate models. This paper presents a simple and
novel approach to determine the number of components and simultaneously estimate the parameters for GMM. By introducing
the degenerating model, the proposed approach overcomes the drawback of likelihood estimate that is a non-decreasing
function and can not be used to select the number of components. The degenerating model is a more general form
of mixture component density and it can degenerate into the component density or a crater-like density when its parameter
K varies from 1 to a bigger value. The likelihood of the crater-like density evaluated for the training data approximates to
zero. This characteristic of the degenerating model forms the foundation of the proposed approach. The experimental
results show robust and evident performance improvement of the approach.
15:00-17:10, Paper MoBT9.3
FIND: A Neat Flip Invariant Descriptor
Guo, Xiaojie, Tianjin Univ.
Cao, Xiaochun, Tianjin Univ.
In this paper, we introduce a novel Flip Invariant Descriptor (FIND). FIND improves the degenerated performance resulted
from image flips and reduces both space and time costs. Flip invariance of FIND enables the intractable flip detection to
be achieved easily, instead of duplicately implementing the procedure. To alleviate the pressure brought by the increasing
scale of image and video data, FIND utilizes a concise structure with less storage space. Comparing to SIFT, FIND reduces
35.94% length for a descriptor. We compare FIND against SIFT with respect to accuracy, speed and space cost. An application
to image search over a database of 3.27 million descriptors is also shown.
15:00-17:10, Paper MoBT9.4
Matching Image with Multiple Local Features
Cao, Yudong, Beijing Univ. of Posts and Telecommunications/ Liaoning Univ. of Tech
Zhang, Honggang, Beijing Univ. of Posts and Telecommunications
Gao, Yanyan, Beijing Univ. of Posts and Telecommunications
Xu, Xiaojun, Beijing Univ. of Posts and Telecommunications
Guo, Jun, Beijing Univ. of Posts and Telecommunications
In this paper, we present the fusional feature composed of Affine-SIFT, MSER and color moment invariants. The fusional
feature is more robust and distinctive than a single local feature. Instead of adding three local features together simply, an
efficient two-level matching strategy is devised with the fusional feature, which speeds up the establishment of the local
correspondences. To remove partial false positives, an affine transformation is estimated with the weighted RANSAC
which decreases iteration times. The experimental results show that our approach can achieve more accurate correspondence.
We prospect to apply the fusional feature and match strategy to image retrieval in the end.
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