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This paper describes a novel multispectral parametric compound Markov random field model for texture synthesis. The proposed compound Markov random field model connects a parametric control random field represented by a hierarchical Potts Markov random field model with analytically solvable wide-sense Markovian representation for single regions. The compound random field synthesis combines the modified...
We propose a probabilistic keyboard based on syllable HMMs, as well as an adaptation for users and operating styles to achieve high accuracy on the software keyboard on mobile devices. The syllable HMMs balances high accuracy by introducing syllabic constraints and word flexibility by not depending on a dictionary. Experimental results showed that a user-dependent probabilistic model reduced the error...
We propose a method to learn and classify pixels in document images, e.g., to separate text from illustrations or other predefined classes. We extract texture information using a bank of Gabor filters, and learn a hierarchical clustering model that can be used as a K-Nearest Neighbours (KNN) classifier. The model has advantages over other local document image classification methods, making it efficient...
Offline Arabic handwritten text recognition task exhibits high variations in observed variables such as size, loops, slant and continuity. Learning algorithm tries to capture the statistical dependence between these variables but often fails to learn the complete distribution because of their large degree-of-freedom. However, it is possible to output a good hypothesis if either data samples for training...
While the performance of Robust Principal Component Analysis (RPCA), in terms of the recovered low-rank matrices, is quite satisfactory to many applications, the time efficiency is not, especially for scalable data. We propose to solve this problem using a novel fast incremental RPCA (FRPCA) approach. The low rank matrices of the incrementally-observed data are estimated using a convex optimization...
Text classification (TC) has long been an important research topic in information retrieval (IR) related areas. Conventional language model (LM)-based TC is solely based on matching the words in the documents and classes by using a naïve Bayes classifier (NBC). In the literature, both the term association model (TA), which further considers word-to-word information, and the relevance model (RM), which...
Mixture models are frequently used to classify data. They are likelihood based models, and the maximum likelihood estimates of parameters are often obtained using the expectation maximization (EM) algorithm. However, multimodality of the likelihood surface means that poorly chosen starting points for optimisation may lead to only a local maximum, not a global maximum. In this paper, different methods...
This paper addresses the problem of identification of pedestrian groups in crowded environments. To that end, positional and directional relations are modeled accounting for different environmental features and group configurations. Subsequently, a pair of simultaneously observed pedestrians is identified to belong to the same group or not utilizing these models in a parallel manner, which defines...
This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance....
In this paper we present a new model for joint extraction of vehicles and coherent vehicle groups in airborne LIDAR point clouds collected from crowded urban areas. Firstly, the 3D point set is segmented into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and intensity values are projected to the ground plane, where the optimal vehicle and...
Capturing appearance of material with respect to illumination and viewing directions is crucial to achieve realistic visual experience in virtual environments. The capturing process is time demanding or requires a specific shape of the captured material. Therefore, we propose a method of such a data reconstruction from very sparse measurements, whose placement allows for continuous and fast acquisition,...
This paper expands the standard pronunciation space (SPS) to include pronunciation errors for automatic pronunciation error detection (APED), uses HMMs to represent the different distributions of pronunciation errors, proposes an adaptive unsupervised clustering of pronunciation errors based on the similarity measures between two HMMs, and then refines more detailed acoustic models for APED within...
Ground-penetrating radar systems are useful for a variety scientific studies, including monitoring changes to the polar ice sheets that may give clues to climate change. A key step in analyzing radar echograms is to identify boundaries between layers of material (such as air, ice, rock, etc.). In this paper, we propose an automated technique for identifying these boundaries, posing this as an inference...
In this paper we present a method of sampling from a probabilistic generative model for a set of graphs. Our method is based on the assumption that the nodes and edges of graphs arise under independent Bernoulli distributions. We sample graphs from the generative model according to the node and edge occurrence probabilities. We explain the construction of our generative model and then compute the...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler...
The evaluation of machine learning algorithms is commonly based on statistical significance tests. However, the suitability of such tests is often questionable. We propose null QQ plots as a simple yet powerful graphical alternative to significance testing. Using ten benchmark data sets, we demonstrate that these plots concisely summarize the essential results from a comparative classification study,...
In this paper, we propose a novel algorithm for the problem of predicting change-points. We assume that the causes for change-points can be characterized by the time interval between a change-point and its symptom. Based on this assumption, we first generate weak classifiers for capturing each characteristic, and then build an ensemble classifier with the weak classifiers. Experimental results show...
We present a new method for the detection of multiple homographies in image pairs. Our aim is to show that we can approach the optimal solution in a short time using an approach based on the well-known RANSAC algorithm. Given feature correspondences between two similar images, our algorithm iteratively generates homography hypotheses using a suitable sampling, optimizes the promising hypotheses and...
In order to estimate multiple structures without prior knowledge of the noise scale, this paper utilizes Jensen-Shannon Divergence (JSD), which is a similarity measurement method, to represent the relations between pairwise data conceptually. This conceptual representation encompasses the geometrical relations between pairwise data as well as the information about whether pairwise data coexist in...
In order to realize model-based 3D object recognition, first, we propose a geometric feature extraction method based on a novel gaze modeling. In the modeling process, local surface models are independently estimated for parts of range data restricted by several gaze domains. Hence, since features are independently extracted from each gaze domain, inconsistent or incorrect features may be obtained...
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