ORCID Profile
0000-0002-7240-3541
Current Organisation
University of Technology Sydney
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Artificial Intelligence and Image Processing | Image Processing | Neural Networks, Genetic Alogrithms And Fuzzy Logic | Computer vision | Computer vision and multimedia computation | Pattern recognition | Computer Vision
Information processing services | Telecommunications | Broadcasting |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: ACM
Date: 22-06-2015
Publisher: Emerald
Date: 13-02-2017
DOI: 10.1108/IJCHM-10-2015-0607
Abstract: The aim of this study is to understand the knowledge-sharing structure and co-production of trip-related knowledge through online travel forums. The travel forum threads were collected from TripAdvisor’s Sydney travel forum for the period from 2010 to 2014, which contains 115,847 threads from 8,346 conversations. The data analytical technique was based on a novel methodological approach – visual analytics, including semantic pattern generation and network analysis. Findings indicate that the knowledge structure is created by community residents who camouflage as local experts and serve as ambassadors of a destination. The knowledge structure presents collective intelligence co-produced by community residents and tourists. Further findings reveal how these community residents associate with each other and form a knowledge repertoire with information covering various travel domain areas. The study offers valuable insights to help destination-management organizations and tour operators identify existing and emerging tourism issues to achieve a competitive destination advantage. This study highlights the process of social media mediated travel knowledge co-production. It also discovers how community residents engage in reaching out to tourists by camouflaging as ordinary users.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2023
Publisher: ACM
Date: 17-10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: IEEE
Date: 11-2014
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 04-05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: IEEE
Date: 07-2017
Publisher: Elsevier BV
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 05-2017
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 12-2019
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 02-2021
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 1997
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: IEEE
Date: 08-2010
DOI: 10.1109/AVSS.2010.76
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: IEEE
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 02-09-2017
Publisher: IEEE
Date: 05-07-2021
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 09-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Elsevier BV
Date: 02-2015
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For ex le, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: IEEE
Date: 10-2008
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11556121_64
Publisher: Association for Computing Machinery (ACM)
Date: 28-10-2014
DOI: 10.1145/2630420
Abstract: With the development of image search technology, users are no longer satisfied with searching for images using just metadata and textual descriptions. Instead, more search demands are focused on retrieving images based on similarities in their contents (textures, colors, shapes etc.). Nevertheless, one image may deliver rich or complex content and multiple interests. Sometimes users do not sufficiently define or describe their seeking demands for images even when general search interests appear, owing to a lack of specific knowledge to express their intents. A new form of information seeking activity, referred to as exploratory search, is emerging in the research community, which generally combines browsing and searching content together to help users gain additional knowledge and form accurate queries, thereby assisting the users with their seeking and investigation activities. However, there have been few attempts at addressing integrated exploratory search solutions when image browsing is incorporated into the exploring loop. In this work, we investigate the challenges of understanding users' search interests from the images being browsed and infer their actual search intentions. We develop a novel system to explore an effective and efficient way for allowing users to seamlessly switch between browse and search processes, and naturally complete visual-based exploratory search tasks. The system, called Browse-to-Search enables users to specify their visual search interests by circling any visual objects in the webpages being browsed, and then the system automatically forms the visual entities to represent users' underlying intent. One visual entity is not limited by the original image content, but also encapsulated by the textual-based browsing context and the associated heterogeneous attributes. We use large-scale image search technology to find the associated textual attributes from the repository. Users can then utilize the encapsulated visual entities to complete search tasks. The Browse-to-Search system is one of the first attempts to integrate browse and search activities for a visual-based exploratory search, which is characterized by four unique properties: (1) in session—searching is performed during browsing session and search results naturally accompany with browsing content (2) in context—the pages being browsed provide text-based contextual cues for searching (3) in focus—users can focus on the visual content of interest without worrying about the difficulties of query formulation, and visual entities will be automatically formed and (4) intuitiveness—a touch and visual search-based user interface provides a natural user experience. We deploy the Browse-to-Search system on tablet devices and evaluate the system performance using millions of images. We demonstrate that it is effective and efficient in facilitating the user's exploratory search compared to the conventional image search methods and, more importantly, provides users with more robust results to satisfy their exploring experience.
Publisher: Elsevier BV
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: IEEE
Date: 2004
Publisher: Informa UK Limited
Date: 12-2011
Publisher: Elsevier BV
Date: 08-2007
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/ICMEW.2012.5
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 03-2012
Publisher: Elsevier BV
Date: 02-2021
Publisher: IEEE
Date: 06-2009
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data ided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 24-07-2023
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 12-2019
Publisher: PeerJ
Date: 03-2021
DOI: 10.7717/PEERJ-CS.382
Abstract: Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize in iduals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.
Publisher: SAGE Publications
Date: 2013
DOI: 10.5772/54566
Publisher: IEEE
Date: 09-2013
Publisher: Springer Science and Business Media LLC
Date: 09-11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Elsevier BV
Date: 09-2019
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: IEEE
Date: 12-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 25-09-2018
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.CMPB.2018.02.011
Abstract: (Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same.
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: IEEE
Date: 09-2011
Publisher: IEEE
Date: 10-2011
Publisher: IEEE
Date: 12-2020
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 18-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2012
Publisher: Elsevier BV
Date: 07-2021
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: IEEE
Date: 10-2008
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Publisher: IEEE
Date: 18-07-2022
Publisher: Elsevier BV
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/ICME.2012.41
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 12-2010
Publisher: ACM
Date: 25-07-2019
Publisher: Springer Singapore
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Elsevier BV
Date: 10-2015
Publisher: IEEE
Date: 10-01-2020
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 11-2021
Publisher: Elsevier BV
Date: 05-1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 1999
Publisher: Elsevier BV
Date: 02-2021
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 11-2016
Publisher: ACM
Date: 17-10-2018
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: IEEE
Date: 16-10-2022
Publisher: Elsevier BV
Date: 10-2014
Publisher: IEEE
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: IEEE
Date: 06-2010
Publisher: American Association for the Advancement of Science (AAAS)
Date: 04-10-2019
Abstract: Bio ersity benefits pollination, pest control, and crop productivity but suffers from land-use intensification.
Publisher: Elsevier BV
Date: 03-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: IEEE
Date: 07-2020
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: The performance of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), \\eg attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. To address this issue, we propose to enhance classifier learning by extracting PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data. In detail, we treat each selected PI as a subcategory and learn one classifier for per subcategory independently. The classifiers for all subcategories are then integrated together to form a more powerful category classifier. Particularly, we propose a new instance-level multi-instance learning (MIL) model to simultaneously select a subset of training images from each subcategory and learn the optimal classifiers based on the selected images. Extensive experiments demonstrate the superiority of our approach.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 11-2006
DOI: 10.1109/AVSS.2006.6
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/49.553683
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Cold Spring Harbor Laboratory
Date: 20-02-2019
DOI: 10.1101/554170
Abstract: Human land use threatens global bio ersity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by few abundant species or rely on high richness remains unclear. Using a global database from 89 crop systems, we partition the relative importance of abundance and species richness for pollination, biological pest control and final yields in the context of on-going land-use change. Pollinator and enemy richness directly supported ecosystem services independent of abundance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the bio ersity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.
Publisher: ACM
Date: 10-2016
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 07-2012
DOI: 10.1109/ICME.2012.36
Publisher: Elsevier BV
Date: 08-2021
Publisher: IEEE
Date: 10-2014
Publisher: ACM
Date: 05-07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 10-2021
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 08-2010
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 03-2018
Publisher: IEEE
Date: 06-2021
Publisher: IEEE
Date: 08-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2009
DOI: 10.1142/S021800140900765X
Abstract: This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/AVSS.2009.41
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/DICTA.2010.5
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/AVSS.2009.44
Publisher: IEEE
Date: 16-10-2022
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 11-2016
Publisher: Springer Science and Business Media LLC
Date: 14-12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2000
DOI: 10.1109/76.845011
Publisher: Elsevier BV
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 12-09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Elsevier BV
Date: 03-2019
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: IEEE
Date: 07-2020
Publisher: Association for Computing Machinery (ACM)
Date: 04-03-2022
DOI: 10.1145/3488715
Abstract: For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and running modes. In addition, relevant experiments on OU-ISIR Treadmill Dataset A have affirmed the effectiveness and feasibility of the proposed method. A state-of-the-art result can be achieved by our proposed method on this dataset.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: ACM
Date: 29-10-2012
Publisher: Elsevier BV
Date: 2018
Publisher: ACM
Date: 12-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: ACM
Date: 12-10-2020
Publisher: ECTI
Date: 17-11-2020
DOI: 10.37936/ECTI-CIT.2021151.240050
Abstract: This paper aims to develop a technique of vessel segmentation in retinal images. Interpreting the segmented vessels is necessary for the automatic detection of the severe stage of the diabetic retinopathy. Thus, it is important to have the technique for segmenting vessels in an automatic way with high performance, for the sake of further analysis. In this paper, the proposed method is developed based on the double layer combining supervised and non-supervised learning aspects. The first layer is to detect the initial seeds of vessels using the supervised learning. It learns based on three types of features including green intensity, line operators, and Gabor filters. Then, the support vector machine (SVM) is applied as the classification tool. In the second layer, the segmentation results from the first layer is further revised and completed using the non-supervised learning. The morphological operations with the watershed technique are applied on the results obtained from the first layer, to remain with the segmented pixels with high confidential to be vessels. Then, these pixels are used as the initial seeds of foreground in the iterative graph cut. As the result, the more completed and comprehensive foreground (i.e. vessels) can be obtained. The proposed method is evaluated using two well-known datasets including DRIVE and STARE. The experimental results show the promising performance of the proposed method when compared with other existing methods in the literature.
Publisher: IEEE
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Elsevier BV
Date: 06-2021
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 09-2013
Publisher: Elsevier BV
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: Institution of Engineering and Technology (IET)
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 06-2016
Start Date: 03-2024
End Date: 03-2027
Amount: $455,969.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2002
End Date: 03-2005
Amount: $67,635.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
End Date: 12-2011
Amount: $280,000.00
Funder: Australian Research Council
View Funded Activity