Abstract book (pdf) - ICPR 2010

In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, 3D-objects are subdivided into a set
of 3D-parts. The subdivision consists to extract from each object a set of feature points with associated curves. Then the whole
set of 3D-parts is clustered into different classes from a semantic point of view. After that, each class is modeled by a Hidden
Markov Model (HMM). The HMM, which represents a character in the alphabet, is trained using the set of curves corresponding
to the class parts. Hence, any 3D-object can be represented by a set of characters. The on-line step consists to compare the set
of characters representing the 3D-object query and that of each object in the given dataset. The experimental results obtained
on the TOSCA dataset show that the system efficiently performs in retrieving similar 3D-models.
13:30-16:30, Paper WeBCT9.7
Fast Fingerprint Retrieval with Line Detection
Lian, Hui-Cheng, Shanghai University
In this paper, a retrieval method is proposed for audio and video fingerprinting systems by adopting a line detection technique.
To achieve fast retrieval, the lines are generated from sub-fingerprints of query and database, and the non-candidate lines are
filtered out. So, the distance between query and refers can be calculated fast. To demonstrate the superiority of this method, the
audio fingerprints and video fingerprints are generated for comparisons. The experimental results indicate that the proposed
method outperforms the direct hashing method.
13:30-16:30, Paper WeBCT9.8
A High-Dimensional Access Method for Approximated Similarity Search in Text Mining
Artigas-Fuentes, Fernando José, Univ. de Oriente, CERPAMID
Badía-Contelles, José Manuel, Univ. Jaume I, Castellón
Gil-García, Reynaldo, Univ. de Oriente, CERPAMID
In this paper, a new access method for very high-dimensional data space is proposed. The method uses a graph structure and
pivots for indexing objects, such as documents in text mining. It also applies a simple search algorithm that uses distance or
similarity based functions in order to obtain the k-nearest neighbors for novel query objects. This method shows a good selectivity
over very-high dimensional data spaces, and a better performance than other state-of-the-art methods. Although it is a probabilistic
method, it shows a low error rate. The method is evaluated on data sets from the well-known collection Reuters corpus
version 1 (RCV1-v2) and dealing with thousands of dimensions.
13:30-16:30, Paper WeBCT9.9
3D Model Comparison through Kernel Density Matching
Wang, Yiming, Nanjing Univ.
Lu, Tong, Nanjing Univ.
Gao, Rongjun, Nanjing Univ.
Liu, Wenyin, City U of HK
A novel 3D shape matching method is proposed in this paper. We first extract angular and distance feature pairs from preprocessed
3D models, then estimate their kernel densities after quantifying the feature pairs into a fixed number of bins. During
3D matching, we adopt the KL-divergence as a distance of 3D comparison. Experimental results show that our method is effective
to match similar 3D shapes, and robust to model deformations or rotation transformations.
13:30-16:30, Paper WeBCT9.10
Improving the Efficiency of Content-Based Multimedia Exploration
Beecks, Christian, RWTH Aachen Univ.
Wiedenfeld, Sascha, RWTH Aachen Univ.
Seidl, Thomas, RWTH Aachen Univ.
Visual exploration systems enable users to search, browse, and explore voluminous multimedia databases in an interactive and
playful manner. Whether users know the database’s contents in advance or not, these systems guide the user’s exploration
process by visualizing the database contents and allowing him or her to issue queries intuitively. In order to improve the efficiency
of content-based visual exploration systems, we propose an efficient query evaluation scheme which aims at reducing the total
number of costly similarity computations. We evaluate our approach on different state-of-the-art image databases.
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