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The impact of global and local features on multiple sequence alignment clustering-based near-duplicate video retrieval

- Wang, Yandan, Lu, Guojun, Belkhatir, Mohammed, Messom, Christopher

  • 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.

Automated multi-sensor color video fusion for nighttime video surveillance

- Ul-Haq, Anwaar, Gondal, Iqbal, Murshed, Manzur

  • Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
  • Date: 2010
  • Type: Text , Conference proceedings
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  • Description: In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.

Detection of multiple dynamic textures using feature space mapping

- Rahman, Ashfaqur, Murshed, Manzur

  • Authors: Rahman, Ashfaqur , Murshed, Manzur
  • Date: 2009
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 19, no. 5 (2009), p. 766-771
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  • Description: Abstract— Image sequences of smoke, fire, etc. are known as dynamic textures. Research is mostly limited to characterization of single dynamic textures. In this paper we address the problem of detecting the presence of multiple dynamic textures in an image sequence by establishing a correspondence between the feature space of dynamic textures and that of their mixture in an image sequence. Accuracy of our proposed technique is both analytically and empirically established with detection experiments yielding 92.5% average accuracy on a diverse set of dynamic texture mixtures in synthetically generated as well as real-world image sequences.

Unitary anomaly detection for ubiquitous safety in machine health monitoring

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Neural Information Processing (INCONIP) p. 361-368
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  • Description: Safety has always been of vital concern in both industrial and home applications. Ensuring safety often requires certain quantifications regarding the inclusive behavior of the system under observation in order to determine deviations from normal behavior. In machine health monitoring, the vibration signal is of great importance for such measurements because it includes abundant information from several machine parts and surroundings that can influence machine behavior. This paper proposes a unitary anomaly detection technique (UAD) that, upon observation of abnormal behavior in the vibration signal, can trigger an alarm with an adjustable threshold in order to meet different safety requirements. The normalized amplitude of spectral contents of the quasi stationary time vibration signal are divided into frequency bins, and the summed amplitudes frequencies over bin are used as features. From a training set consisting of normal vibration signals, Gaussian distribution models are obtained for each feature, which are then used for anomaly detection.

Modelling gene regulatory networks using computational intelligence techniques

- Ram, Ramesh, Chetty, Madhu

  • Authors: Ram, Ramesh , Chetty, Madhu
  • Date: 2010
  • Type: Text , Book chapter
  • Relation: Handbook of Research on Computational Methodologies in Gene Regulatory Networks p. 244-265
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  • Description: This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.

Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

- Stranieri, Andrew, Yatsko, Andrew, Jelinek, Herbert, Venkatraman, Sitalakshmi

  • Authors: Stranieri, Andrew , Yatsko, Andrew , Jelinek, Herbert , Venkatraman, Sitalakshmi
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
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  • Description: Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.

An improved curvature scale-space corner detector and a robust corner matching approach for transformed image identification

- Awrangjeb, Mohammad, Lu, Guojun

  • Authors: Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Image Processing, IEEE Transactions Vol. 17, no. 12 (2008), p. 2425-2441
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  • Description: There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.

REPLOT : REtrieving Profile Links on Twitter for malicious campaign discovery

- Perez, Charles, Birregah, Babiga, Layton, Robert, Lemercier, Marc, Watters, Paul

  • Authors: Perez, Charles , Birregah, Babiga , Layton, Robert , Lemercier, Marc , Watters, Paul
  • Date: 2015
  • Type: Text , Journal article
  • Relation: AI Communications Vol. 29, no. 1 (2015), p. 107-122
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  • Description: Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns. © 2016 - IOS Press and the authors. All rights reserved.

Heuristic non parametric collateral missing value imputation : A step towards robust post-genomic knowledge discovery

- Sehgal, Muhammad Shoaib B, Gondal, Iqbal, Dooley, Laurence, Coppel, Ross

Novel spectral descriptor for object shape

- Sajjanhar, Atul, Lu, Guojun, Zhang, Dengsheng

  • Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng
  • Date: 2010
  • Type: Text , Book chapter
  • Relation: Proceedings of the 11th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing p. 58-67
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  • Description: In this paper, we propose a novel descriptor for shapes. The proposed descriptor is obtained from 3D spherical harmonics. The inadequacy of 2D spherical harmonics is addressed and the method to obtain 3D spherical harmonics is described. 3D spherical harmonics requires construction of a 3D model which implicitly represents rich features of objects. Spherical harmonics are used to obtain descriptors from the 3D models. The performance of the proposed method is compared against the CSS approach which is the MPEG-7 descriptor for shape contour. MPEG-7 dataset of shape contours, namely, CE-1 is used to perform the experiments. It is shown that the proposed method is effective

Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples

- Khandelwal, Manoj, Marto, Aminaton, Fatemi, Seyed, Ghoroqi, Mahyar, Armaghani, Danial, Singh, Trilok, Tabrizi, Omid

  • Authors: Khandelwal, Manoj , Marto, Aminaton , Fatemi, Seyed , Ghoroqi, Mahyar , Armaghani, Danial , Singh, Trilok , Tabrizi, Omid
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 34, no. 2 (2018), p. 307-317
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  • Description: Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R2 = 0.976 and 0.967 for train and test) were better compared to ANN model results (R2 = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples. © 2017, Springer-Verlag London Ltd., part of Springer Nature.

How to improve postgenomic knowledge discovery using imputation

- Sehgal, Muhammad Shoaib B, Gondal, Iqbal, Dooley, Laurence, Coppel, Ross

  • Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Eurasip Journal on Bioinformatics and Systems Biology Vol. 2009, no. 1 (2009), p. 1-14
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  • Description: While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures

Relevance feature mapping for content-based multimedia information retrieval

- Zhou, Guang, Ting, Kaiming, Liu, Fei, Yin, Yilong

  • Authors: Zhou, Guang , Ting, Kaiming , Liu, Fei , Yin, Yilong
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 45, no. 4 (2012), p. 1707-1720
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  • Description: This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.

Some special properties of G A- and LS-based neural learning method

- Ghosh, Ranadhir

  • Authors: Ghosh, Ranadhir
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Intelligent Systems Vol. 14, no. 4 (2005), p. 289-319
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  • Description: Many works in the area of hybrid neural learning algorithms combine global and local based method for artificial neural network. In this paper, we discuss some special properties of a hybrid neural learning algorithm that combines the GA based method with least square based methods such as QR factorization. We look at different types of learning properties of this new hybrid algorithm, such as time complexity, convergence property, and the stability of the algorithm.
  • Description: C1
  • Description: 2003001361

Multimodal image registration technique based on improved local feature descriptors

- Teng, Shyh, Hossain, Tanvir, Lu, Guojun

  • Authors: Teng, Shyh , Hossain, Tanvir , Lu, Guojun
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Journal of Electronic Imaging Vol. 24, no. 1 (2015), p.
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  • Description: Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.

Sustaining the future through virtual worlds

- Gregory, Sue, Gregory, Brent, Hillier, Mathew, Miller, Charlynn, Meredith, Grant

  • Authors: Gregory, Sue , Gregory, Brent , Hillier, Mathew , Miller, Charlynn , Meredith, Grant
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: Future Challenges, Sustainable Futures p. 361-368
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  • Description: Virtual worlds (VWs) continue to be used extensively in Australia and New Zealand higher education institutions although the tendency towards making unrealistic claims of efficacy and popularity appears to be over. Some educators at higher education institutions continue to use VWs in the same way as they have done in the past; others are exploring a range of different VWs or using them in new ways; whilst some are opting out altogether. This paper presents an overview of how 46 educators from some 26 institutions see VWs as an opportunity to sustain higher education. The positives and negatives of using VWs are discussed.

Temporal texture characterization : A review

- Rahman, Ashfaqur, Murshed, Manzur

  • Authors: Rahman, Ashfaqur , Murshed, Manzur
  • Date: 2008
  • Type: Text , Book chapter
  • Relation: Computational Intelligence in Multimedia Processing: Recent Advances p. 291-316
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  • Description: Summary. A large class of objects commonly experienced in a real world scenario exhibits characteristic motion with certain form of regularities. Contemporary literature coined the term “temporal texture”1 to identify image sequences of such motion patterns that exhibit spatiotemporal regularity. The study of temporal textures dates back to the early nineties. Many researchers in the computer vision community have formulated techniques to analyse temporal textures. This chapter aims to provide a comprehensive literature survey of the existing temporal texture characterization technique

DFS based partial pathways in GA for protein structure prediction

- Hoque, Md Tamjidul, Chetty, Madhu, Lewis, Andrew, Sattar, Abdul

  • Authors: Hoque, Md Tamjidul , Chetty, Madhu , Lewis, Andrew , Sattar, Abdul
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Third IAPR International Conference, PRIB 2008
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  • Description: Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations.

Optimizing cepstral features for audio classification

- Fu, Zhouyu, Lu, Guojun, Ting, Kaiming, Zhang, Dengsheng

  • 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.

Building roof plane extraction from LIDAR data

- Awrangjeb, Mohammad, Lu, Guojun

  • Authors: Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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  • Description: This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.