MassBayes: a new generative classifier with multi-dimensional likelihood estimation
- Authors: Aryal, Sunil , Ting, Kaiming
- Date: 2013
- Type: Text , Conference paper
- Relation: Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference p. 136-148
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- Description: Existing generative classifiers (e.g., BayesNet and AnDE) make independence assumptions and estimate one-dimensional likelihood. This paper presents a new generative classifier called MassBayes that estimates multi-dimensional likelihood without making any explicit assumptions. It aggregates the multi-dimensional likelihoods estimated from random subsets of the training data using varying size random feature subsets. Our empirical evaluations show that MassBayes yields better classification accuracy than the existing generative classifiers in large data sets. As it works with fixed-size subsets of training data, it has constant training time complexity and constant space complexity, and it can easily scale up to very large data sets.
Maximizing structural similarity in multimodal biomedical microscopic images for effective registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
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- Description: Multimodal image registration (MMIR) is the alignment of contents in images captured from different sensors or instruments. MMIR is important in medical applications as it enables the visualization of the complementary contents in biomedical microscopic images. The registration for such images can be challenging as the structures of their contents are usually only partially similar. Thus in this paper, we propose a new method to maximize the structural similarity of the contents in such images by utilizing intensity relationships among Red-Green-Blue color channels. Our experimental results will demonstrate that our proposed method substantially improves the accuracy of registering such images as compared to the state-of-the-art methods.
mDBN: motif based learning of gene regulatory networks using dynamic Bayesian networks
- Authors: Morshed, Nizamul , Chetty, Madhu , Nguyen, Vinh , Caelli, Terry
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO'13, Association for Computing Machinery Inc. (ACM), 2013 p. 279-286
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- Description: Solutions for deriving the most consistent Bayesian gene regulatory network model from given data sets using evolutionary algorithms typically only result in locally optimal solutions.
On temporal order invariance for view-invariant action recognition
- Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 23, no. 2 (2013), p. 203-211
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- Description: View-invariant action recognition is one of the most challenging problems in computer vision. Various representations are being devised for matching actions across different viewpoints to achieve view invariance. In this paper, we explore the invariance property of temporal order of action instances during action execution and utilize it for devising a new view-invariant action recognition approach. To ensure temporal order during matching, we utilize spatiotemporal features, feature fusion and temporal order consistency constraint. We start by extracting spatiotemporal cuboid features from video sequences and applying feature fusion to encapsulate within-class similarity for the same viewpoints. For each action class, we construct a feature fusion table to facilitate feature matching across different views. An action matching score is then calculated based on global temporal order constraint and number of matching features. Finally, the action label of the class with the maximum value of the matching score is assigned to the query action. Experimentation is performed on multiple view Inria Xmas motion acquisition sequences and West Virginia University action datasets, with encouraging results, that are comparable to the existing view-invariant action recognition techniques.
Optimizing cepstral features for audio classification
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2013
- Type: Text , Conference paper
- Relation: International Joint Conference on Artificial Intelligence p. 1330-1336
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- Description: Cepstral features have been widely used in audio applications. Domain knowledge has played an important role in designing different types of cepstral features proposed in the literature. In this paper, we present a novel approach for learning optimized cepstral features directly from audio data to better discriminate between different categories of signals in classification tasks. We employ multi-layer feedforward neural networks to model the cepstral feature extraction process. The network weights are initialized to replicate a reference cepstral feature like the mel frequency cepstral coefficient. We then propose a embedded approach that integrates feature learning with the training of a support vector machine (SVM) classifier. A single optimization problem is formulated where the feature and classifier variables are optimized simultaneously so as to refine the initial features and minimize the classification risk. Experimental results have demonstrated the effectiveness of the proposed feature learning approach, outperforming competing methods by a large margin on benchmark data.
Perception-inspired background subtraction
- Authors: Haque, Mahfuzul , Murshed, Manzur
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 23, no. 12 (2013 2013), p. 2127-2140
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- Description: Developing universal and context-invariant methods is one of the hardest challenges in computer vision. Background subtraction (BS), an essential precursor in most machine vision applications used for foreground detection, is no exception. Due to overreliance on statistical observations, most BS techniques show unpredictable behavior in dynamic unconstrained scenarios in which the characteristics of the operating environment are either unknown or change drastically. To achieve superior foreground detection quality across unconstrained scenarios, we propose a new technique, called perception-inspired background subtraction (PBS), which avoids overreliance on statistical observations by making key modeling decisions based on the characteristics of human visual perception. PBS exploits the human perception-inspired confidence interval to associate an observed intensity value with another intensity value during both model learning and background-foreground classification. The concept of perception-inspired confidence interval is also used for identifying redundant samples, thus ensuring the optimal number of samples in the background model. Furthermore, PBS dynamically varies the model adaptation speed (learning rate) at pixel level based on observed scene dynamics to ensure faster adaptation of changed background regions, as well as longer retention of stationary foregrounds. Extensive experimental evaluations on a wide range of benchmark datasets validate the efficacy of PBS compared to the state of the art for unconstraint video analytics.
Predictive coding of integers with real-valued predictions
- Authors: Ali, Mortuza , Murshed, Manzur
- Date: 2013
- Type: Text , Conference paper
- Relation: DCC 2013 Data Compression Conference; Snowbird, USA; 20th-22nd March 2013; p. 431-440
- Relation: http://purl.org/au-research/grants/arc/DP130103670
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- Description: In this paper, we have extended the Rice-Golomb code so that it can operate at fractional precision to efficiently exploit the real-valued predictions. Coding at infinitesimal precision allows the residuals to be modeled with the Lap lace distribution. Unlike the Rice-Golomb code, which maps equally probable opposite-signed residuals to different integers, the proposed coding scheme is symmetric in the sense that, at infinitesimal precision, it assigns code words of equal length to equally probable residual intervals. The symmetry of both the Lap lace distribution and the coding scheme facilitates the analysis of the proposed coding scheme to determine the average code-length and the optimal value of the associated coding parameter.
Regularly frequent patterns mining from sensor data stream
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Conference paper
- Relation: International Conference on Neural Information Processing (ICONIP 2013) p. 417-424
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- Description: Mining interesting and useful knowledge from the huge amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature use support metric-based sensor association rule which employs the occurrence frequency of patterns as criteria. Such criteria may not be appropriate for finding significant patterns. Moreover, temporal regularity in occurrence behavior should be considered as another important measure for assessing the importance of patterns in WSNs. Frequent sensor patterns that occur after regular intervals is called regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a novel tree structure called Regularly Frequent Sensor Pattern-tree (RSP-tree) and an efficient mining approach for finding regularly frequent sensor patterns from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding regularly frequent sensor patterns.
Rhythmic and sustained oscillations in metabolism and gene expression of Cyanothece sp. ATCC 51142 under constant light
- Authors: Gaudana, Sandeep , Krishnakumar, S. , Alagesan, Swathi , Digmurti, Madhuri , Viswanathan, Ganesh , Chetty, Madhu , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: Frontiers in Microbiology Vol. 4, no. Article 374 (2013), p. 1-11
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- Description: Cyanobacteria, a group of photosynthetic prokaryotes, oscillate between day and night time metabolisms with concomitant oscillations in gene expression in response to light/dark cycles (LD). The oscillations in gene expression have been shown to sustain in constant light (LL) with a free running period of 24 h in a model cyanobacterium Synechococcus elongatus PCC 7942. However, equivalent oscillations in metabolism are not reported under LL in this non-nitrogen fixing cyanobacterium. Here we focus on Cyanothece sp. ATCC 51142, a unicellular, nitrogen-fixing cyanobacterium known to temporally separate the processes of oxygenic photosynthesis and oxygen-sensitive nitrogen fixation. In a recent report, metabolism of Cyanothece 51142 has been shown to oscillate between photosynthetic and respiratory phases under LL with free running periods that are temperature dependent but significantly shorter than the circadian period. Further, the oscillations shift to circadian pattern at moderate cell densities that are concomitant with slower growth rates. Here we take this understanding forward and demonstrate that the ultradian rhythm under LL sustains at much higher cell densities when grown under turbulent regimes that simulate flashing light effect. Our results suggest that the ultradian rhythm in metabolism may be needed to support higher carbon and nitrogen requirements of rapidly growing cells under LL. With a comprehensive Real time PCR based gene expression analysis we account for key regulatory interactions and demonstrate the interplay between clock genes and the genes of key metabolic pathways. Further, we observe that several genes that peak at dusk in Synechococcus peak at dawn in Cyanothece and vice versa. The circadian rhythm of this organism appears to be more robust with peaking of genes in anticipation of the ensuing photosynthetic and respiratory metabolic phases.
Structural image retrieval using automatic image annotation and region based inverted file
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2013), p. 1087-1098
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- Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.
The impact of global and local features on multiple sequence alignment clustering-based near-duplicate video retrieval
- Authors: Wang, Yandan , Lu, Guojun , Belkhatir, Mohammed , Messom, Christopher
- Date: 2013
- Type: Text , Conference paper
- Relation: 14th Pacific-Rim Conference on Multimedia p. 669-677
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- Description: Traditionally, the performance of Near-Duplicate Video Retrieval (NDVR) is enhanced through different video features, matching scheme and indexing methods. The video features have been intensively investigated and it has been shown that local features outperform global features in terms of accuracy. However, local features have the expensive computational problem. Therefore, indexing structure is introduced to assist in scaling up, whilst the accuracy will drop slightly or dramatically in most time by using indexing approaches. Recent progress shows that NDVR based on clustering could reduce searching space while maintains equivalent retrieval accuracy compared to that of non-clustering based. In this paper, we will continue to evaluate clustering based NDVR, but using popular global and local features. Before conducting NDVR, dataset will be pre-processed offline into groups by using clustering algorithm that near-duplicate videos (NDVs) are assembled in the same cluster. Each cluster will be represented by member video or the centroid. The query video will then be compared to the representative videos instead of all videos in database (non-clustering based). Our experiment shows that clustering-based NDVR using global and local features outperforms than that of non-clustering based in terms of both retrieval accuracy and speed.
A review on automatic image annotation techniques
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 45, no. 1 (2012), p. 346-362
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- Description: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
Achieving high multi-modal registration performance using simplified Hough-transform with improved symmetric-SIFT
- Authors: Hossain, Md Tanvir , Teng, Shyh , Lu, Guojun
- Date: 2012
- Type: Text , Conference paper
- Relation: 14th International Conference on Digital Image Computing Techniques and Applications, DICTA 2012
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- Description: The traditional way of using Hough Transform with SIFT is for the purpose of reliable object recognition. However, it cannot be effectively applied to image registration in the same way as the recall rate can be significantly lower. In this paper, we propose an alternative implementation of Hough Transform that can be used with Improved Symmetric-SIFT for multi-modal image registration. Our experimental results show that the proposed technique of applying Hough Transform can significantly improve the key-point matching as well as registration accuracy by utilizing aggregated information from key-points throughout the input images.
An annotation rule extraction algorithm for image retrieval
- Authors: Chen, Zeng , Hou, Jin , Zhang, Dengsheng , Qin, Xue
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 33, no. 10 (2012), p.1257-1268
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- Description: Automatic image annotation can be used to facilitate semantic search in large image databases. However, retrieval performance of the existing annotation schemes is far from the users’ expectation. In this paper, we propose a novel method to automatically annotate image through the rules generated by support vector machines and decision trees. In order to obtain the rules, we collect a set of training regions by image segmentation, feature extraction and discretization. We first employ a support vector machine as a preprocessing technique to refine the input training data and then use it to improve the rules generated by decision tree learning. The preprocessing can effectively deal with the similar regions in an image as well. Moreover, we integrate the original rules to the modified ones, so as to formulate the complete and effective annotation rules. We can translate an unknown image into text by this algorithm, and the proposed system can retrieve images queried by both images and keywords. Experiments are carried out in a standard Corel dataset and images collected from the Web to test the accuracy and robustness of the proposed system. Experimental results show the proposed algorithm can annotate and retrieve images more efficiently than traditional learning algorithms.
An intelligent approach to evaluate drilling performance
- Authors: Bhatnagar, Anupam , Khandelwal, Manoj
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 21, no. 4 (2012), p. 763-770
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- Description: In this paper, an attempt has been made to predict the rate of penetration (ROP) of rocks by incorporating thrust, revolutions per minute (rpm), flushing media and compressive strength of rocks using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 4-7-1 architecture was trained using 472 experimental data sets of sandstone, limestone, rock phosphate, dolomite, marble and quartz-chlorite-schist rocks. A total of 146 new data sets were used for the testing and comparison of the ROP by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of ROP. The coefficient of determination by ANN was 0. 985, while coefficient of determination was 0. 629 for rate of penetration. The mean absolute error (MAE) for rate of penetration by ANN was 0. 3547, whereas MAE by MVRA was 1. 7499. © 2010 Springer-Verlag London Limited.
Application of an expert system to predict thermal conductivity of rocks
- Authors: Khandelwal, Manoj
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1341-1347
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- Description: In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by SVM and MVRA was 0. 994 and 0. 918, respectively, whereas MAE was 0. 0453 and 0. 2085 for SVM and MVRA, respectively. © 2011 Springer-Verlag London Limited.
Application of artificial intelligence to improve quality of service in computer networks
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol. 21, no. 1 (2012), p. 81-90
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- Description: Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.
Artificial neural network for prediction of air flow in a single rock joint
- Authors: Ranjith, Pathegama , Khandelwal, Manoj
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1413-1422
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- Reviewed:
- Description: In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0. 995 and 0. 758 by ANN and MVRA, respectively, whereas MAE was 0. 0413 and 0. 1876. © 2011 Springer-Verlag London Limited.
Data discretization for dynamic Bayesian network based modeling of genetic networks
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Conference paper
- Relation: Neural Information Processing 19th International Conference p. 298-306
- Full Text: false
- Reviewed:
- Description: Dynamic Bayesian networks (DBN) are widely applied in Systems biology for modeling various biological networks, including gene regulatory networks and metabolic networks. The application of DBN models often requires data discretization. Although various discretization techniques exist, currently there is no consensus on which approach is most suitable. Popular discretization strategies within the bioinformatics community, such as interval and quantile discretization, are likely not optimal. In this paper, we propose a novel approach for data discretization for mutual information based learning of DBN. In this approach, the data are discretized so that the mutual information between parent and child nodes is maximized, subject to a suitable penalty put on the complexity of the discretization. A dynamic programming approach is used to find the optimal discretization threshold for each individual variable. Our approach iteratively learns both the network and the discretization scheme until a locally optimal solution is reached. Tests on real genetic networks confirm the effectiveness of the proposed method.
Efficient pattern index coding using syndrome coding and side information
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Engineering and Industries Vol. 3, no. 3 (2012), p. 1-12
- Full Text: false
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- Description: Pattern-based video coding focusing on moving regions has already established its superiority over the H.264 at very low bit rate. Up to a certain limit, the larger the number of pattern templates, thebetter the approximation to the moving regions. However, beyond that limit no coding gain is observed due to the need of an excessive number of pattern identification bits. Recently, distributed video codingschemes have used syndrome coding to predict the original information in the decoder using side information. In this paper a pattern identification scheme is proposed which predicts the pattern fromthe syndrome codes and side information in the decoder so that the actual pattern identification code is not needed. The experimental results confirm that the new scheme improves the rate-distortionperformance compared to the existing pattern-based video coding and compared with the H.264 standard. The proposed new scheme will also present opportunities for further syndrome codingapplication.