Abstract book (pdf) - ICPR 2010

Müller, Henning, Univ. of Applied Sciences Sierre, Switzerland
In this paper, classical approaches such as maximum combinations (combMAX), sum combinations (comb-SUM) and
the product of the maximum and a nonzero number (combMNZ) were employed and the trade off between two fusion effects
(chorus and dark horse effects) was studied based on the sum of n maximums. Various normalization strategies were
tried out. The fusion algorithms are evaluated using the best four visual and textual runs of the ImageCLEF medical image
retrieval task 2008 and 2009. The results show that fused runs outperform the best original runs and multi-modality fusion
statistically outperforms single modality fusion. The logarithmic rank penalization shows to be the most stable normalization.
The dark horse effect is in competition with the chorus effect and each of them can produce best fusion performance
depending on the nature of the input data.
16:40-17:00, Paper TuCT5.4
Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor
Rusiñol, Marçal, Univ. Autònoma de Barcelona
Nourbakhsh, Farshad, Computer Vision Center / Univ. Autònoma de Barcelona
Karatzas, Dimosthenis, Univ. Autonoma de Barcelona
Valveny, Ernest, Computer Vision Center / Univ. Autònoma de Barcelona
Llados, Josep, Computer Vision Center
In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is
added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues.
We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed
method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color
trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms
the sole use of the shape context descriptor.
17:00-17:20, Paper TuCT5.5
Weighted Boundary Points for Shape Analysis
Zhang, Jing, Univ. of South Florida
Kasturi, Rangachar, Univ. of South Florida
Shape analysis is an active and important branch in computer vision research field. In recent years, many geometrical, topological,
and statistical features have been proposed and widely used for shape-related applications. In this paper, based on
the properties of Distance Transform, we present a new shape feature, weight of boundary point. By computing the shortest
distances between boundary points and distance contours of a transformed shape, every boundary point is assigned a weight,
which contains the interior structure information of the shape. To evaluate the proposed new shape feature, we tested the
weighted boundary points on shape matching and shape decomposition. The experimental results demonstrated the validity.
TuCT6 Dolmabahçe Hall B
Speech and Speaker Recognition Regular Session
Session chair: Shinoda, Koichi (Tokyo Institute of Technology)
15:40-16:00, Paper TuCT6.1
Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition
Stadelmann, Thilo, Univ. of Marburg
Freisleben, Bernd, Univ. of Marburg
The Gaussian Mixture Model (GMM) is often used in conjunction with Mel-frequency cepstral coefficient (MFCC) feature
vectors for speaker recognition. A great challenge is to use these techniques in situations where only small sets of training
and evaluation data are available, which typically results in poor statistical estimates and, finally, recognition scores. Based
on the observation of marginal MFCC probability densities, we suggest to greatly reduce the number of free parameters in
the GMM by modeling the single dimensions separately after proper preprocessing. Saving about 90% of the free parameters
as compared to an already optimized GMM and thus making the estimates more stable, this approach considerably improves
recognition accuracy over the baseline as the utterances get shorter and saves a huge amount of computing time both in training
and evaluation, enabling real-time performance. The approach is easy to implement and to combine with other short-utterance
approaches, and applicable to other features as well.
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