This paper proposes a food image classification method using local textural patterns and their gl... more This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature ...
This paper proposes a part-based template matching method for multi-view human detection. The pro... more This paper proposes a part-based template matching method for multi-view human detection. The proposed method includes two stages: matching and verification. In particular, the best individual matching parts given a detection window are determined ...
We study indices for choosing the correct number of components in a mixture of normal distributi... more We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn’s index.
This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR ima... more This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR images without any prior knowledge about the number of tissues and their density distributions on each MRI echo. The brain tissues are described by a finite Gaussian mixture model (FGMM). The FGMM parameters are learned by sequentially applying the expectation maximization (EM) algorithm to a stream of data sets which are specifically organized according to the global spatial relationship of the brain tissues. Preliminary results on actual MRI slices have shown the method to be promising
The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied ... more The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied for the purpose of unsupervised data clustering. This method is applied to segmentation of grey-scale digital images, and the properties of the map are illustrated by clustering of uniformly distributed two-dimensional squares
HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University ... more HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University of Sydney. Ling Guan. Ryerson Polytechnic University. Xing Zhang, WanQing Li. Motorola Australian Research Center. ... Abstract: We propose a method for extracting ...
The hierarchical cluster model (HCM), a neural network inspired by the human brain (see Sutton, J... more The hierarchical cluster model (HCM), a neural network inspired by the human brain (see Sutton, J., Harvard Medical School, MIT, Neural Systems Group, Technical Report, 1995), is demonstrated for the purpose of region segmentation in digital images. Starting with an over segmented image, regions are merged based on evidence of a valid edge between the two regions. Unlike Sutton's work, in which the HCM is used to recall a set of pre-trained memory patterns, the HCM in our work demonstrates unsupervised decision making capabilities.
The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied ... more The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied for the purpose of unsupervised data clustering. This method is applied to segmentation of grey-scale digital images, and the properties of the map are illustrated by clustering of uniformly distributed two-dimensional squares
HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University ... more HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University of Sydney. Ling Guan. Ryerson Polytechnic University. Xing Zhang, WanQing Li. Motorola Australian Research Center. ... Abstract: We propose a method for extracting ...
This paper proposes a food image classification method using local textural patterns and their gl... more This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature ...
This paper proposes a part-based template matching method for multi-view human detection. The pro... more This paper proposes a part-based template matching method for multi-view human detection. The proposed method includes two stages: matching and verification. In particular, the best individual matching parts given a detection window are determined ...
We study indices for choosing the correct number of components in a mixture of normal distributi... more We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn’s index.
This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR ima... more This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR images without any prior knowledge about the number of tissues and their density distributions on each MRI echo. The brain tissues are described by a finite Gaussian mixture model (FGMM). The FGMM parameters are learned by sequentially applying the expectation maximization (EM) algorithm to a stream of data sets which are specifically organized according to the global spatial relationship of the brain tissues. Preliminary results on actual MRI slices have shown the method to be promising
The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied ... more The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied for the purpose of unsupervised data clustering. This method is applied to segmentation of grey-scale digital images, and the properties of the map are illustrated by clustering of uniformly distributed two-dimensional squares
HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University ... more HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University of Sydney. Ling Guan. Ryerson Polytechnic University. Xing Zhang, WanQing Li. Motorola Australian Research Center. ... Abstract: We propose a method for extracting ...
The hierarchical cluster model (HCM), a neural network inspired by the human brain (see Sutton, J... more The hierarchical cluster model (HCM), a neural network inspired by the human brain (see Sutton, J., Harvard Medical School, MIT, Neural Systems Group, Technical Report, 1995), is demonstrated for the purpose of region segmentation in digital images. Starting with an over segmented image, regions are merged based on evidence of a valid edge between the two regions. Unlike Sutton's work, in which the HCM is used to recall a set of pre-trained memory patterns, the HCM in our work demonstrates unsupervised decision making capabilities.
The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied ... more The self-organising tree map (SOTM), which is a variation of the self-organising map, is studied for the purpose of unsupervised data clustering. This method is applied to segmentation of grey-scale digital images, and the properties of the map are illustrated by clustering of uniformly distributed two-dimensional squares
HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University ... more HIERARCHICAL CLUSTER MODEL FOR PERCEPTUAL IMAGE PROCESSING. ... Jonathan Randall. The University of Sydney. Ling Guan. Ryerson Polytechnic University. Xing Zhang, WanQing Li. Motorola Australian Research Center. ... Abstract: We propose a method for extracting ...
The advance of the Internet in the past decade has radically changed the way people communicate a... more The advance of the Internet in the past decade has radically changed the way people communicate and collaborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be considered and leveraged in the online world. The rapid deployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ‘connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.
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