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In this work, we propose a novel multilingual word spotting framework based on Hidden Markov Models that works on corpus of multilingual handwritten documents and documents that contain more than one handwritten script. The system deals with large multilingual vocabularies without need for word or character segmentation. A keyword is represented by concatenating its character models. We propose and...
Automatic age estimation is the process of using a computer to predict the age of a person automatically based on a given facial image. While this problem has numerous real-world applications, the high variability of aging patterns and the sparsity of available data present challenges for model training. Here, instead of training one global aging function, we train an individual function for each...
In likelihood ratio based decision methods, often a variable number of input evidences is used. A decision based on many such inputs can result in nearly the same likelihood ratio as one based on few inputs. We consider methods for distinguishing between such situations. One of these is to provide confidence intervals together with the decisions and another is to combine the inputs using weights....
We propose an approach to recognize group activities which involve several persons based on modeling the interactions between human bodies. Benefitted from the recent progress in pose estimation [1], we model the activities as the interactions between the parts belong to the same person (intra-person) and those between the parts of different persons (inter-person). Then a unified, discriminative model...
Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical...
A simple and efficient skin detector facilitates automatic and robust human detection and tracking. In this paper, we propose a new skin detection method via linear regression tree, which decomposes the problem of discriminating different skin and nonskin colors into several simple problems. Experimental results on the MCG skin database demonstrated its better generalization ability and discriminability...
Prognosis refers to the prediction of the future health status of a patient. Providing prognostic insight to clinicians is critical for physician decision support. In this paper we present a collaborative disease prognosis strategy leveraging the information of the clinically similar patient cohort, using a Local Spline Regression (LSR) based similarity measure. To improve the reliability of the approach,...
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 an approach for automated mitosis detection, which provides critical information during performing breast cancer prognosis. Essentially, the problem of mitotic detection involves irregular shape object classification. It is a very challenging task. In this paper, a novel algorithm, named eXclusive Independent Component Analysis (XICA) is proposed. The XICA is an extension...
Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm...
This paper proposes a new framework of band selection for object classification in hyperspectral images. Different from traditional approaches where the selected bands are shared from all classes, in this work, different subsets of bands are selected for different class pairs. Without prior knowledge of spectral database, we estimate the spectral characteristic of objects with the labeled and unlabeled...
Many object recognition or concept identification tasks require accurate detection of large number of classes. These applications present enormous challenges to traditional classification methods, which are mostly designed for solving problems with small number of classes. In this paper, we develop a method called recursive non-negative matrix factorization (RNMF) for building a hierarchical label...
Among ensemble learning methods, stacking with a meta-level classifier is frequently adopted to fuse the output of multiple base-level classifiers and generate a final score. Labeled data is usually split for basetraining and meta-training, so that the meta-level learning is not impacted by over-fitting of base level classifiers on their training data. We propose a novel knowledge-transfer framework...
When a multi-label classifier outputs a real-valued score for each class, a well known design strategy consists of tuning the corresponding decision thresholds by optimising the performance measure of interest on validation data. In this paper we focus on the F-measure, which is widely used in multi-label problems. We derive two properties of the micro-averaged F measure, viewed as a function of the...
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small...
Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) achieved state-of-the-art performance in image classification. However, only adopting SPM to exploit spatial information is not enough for satisfactory performance. In this paper, we use hierarchical temporal memory (HTM) cortical learning algorithms to extend this LLC...
This paper addresses the problem of binary classifier learning when the training data is imbalanced, i.e. the samples of the two classes have significantly different cardinality. We investigate two different cost-sensitive approaches in the conditional mutual information (CMI) based weak classifier selection procedure using histogram descriptors. The first method uses CMI for classifier selection,...
The representative samples can be pictured as the skeleton of a point cloud. We learn a discrete distribution defined over all samples, so that these skeleton points have large probabilities and the outliers have probabilities close to zero. The basic assumption is that any observation is generated from a nearby skeleton point. The learning objective is to minimize the communication cost from a random...
Nearest-Neighbor based Image Classification (N-NIC) has drawn considerable attention in the past several years because it does not require classifier training. Similar to an orderless Bag-of-Feature image representation, the traditional NNIC ignores global geometric correspondence. In this paper, we present a technique to exploit the global geometric correspondence in a nearest neighbor classifier...
The mathematical modeling of classifier has been intensively investigated in pattern recognition for decades. Maximin classifier, which conducts optimization based on the perpendicularly closest data point(s) to the decision boundary, has been widely used. However, such method may lead to inferior performance when the boundary data point(s) is significantly influenced by noise. This paper presents...
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