An efficient algorithm for realizing matching pursuits and its applications in MPEG4 coding system

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2000-2001 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Temporal Exemplar-based Bayesian Networks for facial expression recognition

Proceedings of the International Conference on Machine Learning and Applications, 2008, p. 16-22We present a Temporal Exemplar-based Bayesian Networks (TEBNs) far facial expression recognition. The proposed Bayesian Networks (BNs) consists of three layers: Observation layer, Exemplars layer and Prior Knowledge layer. In the Exemplars layer, exemplar-based model is integrated with BNs to improve the accuracy of probability estimation. In the Prior Knowledge layer, static BNs is extended to Temporal BNs by considering historical observations to model temporal behavior of facial expression. Experiment on CMU expression database illustrates that the proposed TEBNs is very efficient in modeling the evolution of facial deformation. © 2008 IEEE.published_or_final_versio

A temporal latent topic model for facial expression recognition

Posters: no. 128LNCS v. 6495 is conference proceedings of the 10th Asian Conference on Computer Vision, Queens, ACCVIn this paper we extend the latent Dirichlet allocation (LDA) topic model to model facial expression dynamics. Our topic model integrates the temporal information of image sequences through redefining topic generation probability without involving new latent variables or increasing inference difficulties. A collapsed Gibbs sampler is derived for batch learning with labeled training dataset and an efficient learning method for testing data is also discussed. We describe the resulting temporal latent topic model (TLTM) in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed TLTM is very efficient in facial expression recognition. © 2011 Springer-Verlag Berlin Heidelberg.postprintThe 10th Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6495, p. 51-6

The Majority Report - Can we use big data to secure a better future?

With the widely adopted use of social media, it now becomes a common platform for calling supporters for civil unrest events. Despite the noble aims of these civil unrest events, sometimes these events might turn violent and disturb the daily lives of the general public. This paper aims to propose a conceptual framework regarding the study of using online social media data to predict offline civil unrest events. We propose to use time-series metrics as the prediction attributes instead of analyzing message contents because the message contents on social media are usually noisy, informal and not so easy to interpret. In the case of a data set containing both civil unrest event dates and normal dates, we found that it contains many more samples from the normal dates class than from the civil unrest event dates class. Thus, creating an imbalanced class problem. We showed using accuracy as the performance metrics could be misleading as civil unrest events were the minority class. Thus, we suggest to use additional tactics to handle the imbalanced class prediction problem. We propose to use a combination of oversampling the minority class and using feature selection techniques to tackle the imbalanced class problem. The current results demonstrate that use of time-series metrics to predict civil unrest events is a possible solution to the problems of handling the noise and unstructured format of social media data contents in the process of analysis and predictions. In addition, we have showed that the combination of special techniques to handle imbalanced class outperformed other classifiers without using such techniques.published_or_final_versio

Nonparametric discriminant HMM and application to facial expression recognition

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, p. 2090-2096This paper presents a nonparametric discriminant HMM and applies it to facial expression recognition. In the proposed HMM, we introduce an effective nonparametric output probability estimation method to increase the discrimination ability at both hidden state level and class level. The proposed method uses a nonparametric adaptive kernel to utilize information from all classes and improve the discrimination at class level. The discrimination between hidden states is increased by defining membership coefficients which associate each reference vector with hidden states. The adaption of such coefficients is obtained by the Expectation Maximization (EM) method. Furthermore, we present a general formula for the estimation of output probability, which provides a way to develop new HMMs. Finally, we evaluate the performance of the proposed method on the CMU expression database and compare it with other nonparametric HMMs. © 2009 IEEE.published_or_final_versio

Supervised learning of the adaptive resonance theory system

A supervised learning ART model (SART) is proposed which is based on the structure of ARTMAP but is much simpler. The techniques of match tracking and complement coding have been implemented to ensure the correct selection of category and stability during the training and testing phases. Two simulations have been done in order to verify and evaluate the classification power of SART. The result of identification of poisonous mushroom by SART is compared with that by ARTMAP.published_or_final_versio

A fast two-stage algorithm for realizing matching pursuit

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringVersion of RecordPublishe

Replication of H9 influenza viruses in the human ex vivo respiratory tract, and the influence of neuraminidase on virus release

H9N2 viruses are the most widespread influenza viruses in poultry in Asia. We evaluated the infection and tropism of human and avian H9 influenza virus in the human respiratory tract using ex vivo respiratory organ culture. H9 viruses infected the upper and lower respiratory tract and the majority of H9 viruses had a decreased ability to release virus from the bronchus rather than the lung. This may be attributed to a weak neuraminidase (NA) cleavage of carbon-6-linked sialic acid (Sia) rather than carbon-3-linked Sia. The modified cleavage of N-acetlylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) by NA in H9 virus replication was observed by reverse genetics, and recombinant H9N2 viruses with amino acids (38KQ) deleted in the NA stalk, and changing the amino acid at position 431 from Proline-to-Lysine. Using recombinant H9 viruses previously evaluated in the ferret, we found that viruses which replicated well in the ferret did not replicate to the same extent in the human ex vivo cultures. The existing risk assessment models for H9N2 viruses in ferrets may not always have a strong correlation with the replication in the human upper respiratory tract. The inclusion of the human ex vivo cultures would further strengthen the future risk-assessment strategies.published_or_final_versio

Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps

So far the bottleneck of Chinese recognition, especially handwritten recognition, still lies in the effectiveness of feature-extraction to cater for various distortions and position shifting. In the paper, a novel method is proposed by applying a set of Gabor spatial filters with different directions and spatial frequencies to character images, in an effort to reach the optimum trade-off between feature stability and feature localization. While a classic self-organizing map is used for unsupervised clustering feature codes, a multi-staged LVQ with a fuzzy judgement unit is applied for the final recognition on the feature mapping result.published_or_final_versio

Consistent relaxation matching for handwritten Chinese character recognition

Due to the complexity in structure and the various distortions (translation, rotation, shifting, and deformation) in different writing styles of Handwritten Chinese Characters(HCCs), it is more suitable to use a structural matching algorithm for computer recognition of HCC. Relaxation matching is a powerful technique which can tolerate considerable distortion. However, most relaxation techniques so far developed for Handwritten Chinese Character Recognition (HCCR) are based on a probabilistic relaxation scheme. In this paper, based on local constraint of relaxation labelling and optimization theory, we apply a new relaxation matching technique to handwritten character recognition. From the properties of the compatibility constraints, several rules are devised to guide the design of the compatibility function, which plays an important role in the relaxation process. By parallel use of local contextual information of geometric relaxationship among strokes of two characters, the ambiguity between them can be relaxed iteratively to achieve optimal consistent matching.published_or_final_versio