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Automated learning systems used to extract useful information from musical scripts, play a major role in optical music recognition. Optical music recognition or OMR has been widely used to extract the musical notations and knowledge from old scripts and thus enclose lot of importance in retrieving historical data. The field of pattern recognition and knowledge representation has to be symmetrically...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more efficient, problem-driven, and engaging. Utilizing a sequence segmentation task with a Hidden Markov Model...
Segmenting range data into semantic categories has become a more and more active field of research in robotics. In this paper, we advocate to view this task as a problem of fast, large-scale retrieval. Intuitively, given a dataset of millions of labeled scan points and their neighborhoods, we simply search for similar points in the datasets and use the labels of the retrieved ones to predict the labels...
Electronic vehicle guidance systems have gained much popularity over the last years. The massive use of inexpensive global positioning system receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large amounts of data combining both modalities will be available in a near future. The approach presented here draws on machine learning techniques...
Indirect immunofluorescence (IIF) with HEp-2 cells has been used to detect antinuclear auto-antibodies (ANA) for diagnosing systemic autoimmune diseases. The aim of this study is to develop an automatic scheme to identify the fluorescence pattern of HEp-2 cell in the IIF images. By using the previously proposed two-staged segmentation method, the similarity-based watershed algorithm with marker techniques...
In this paper, we present an active boosting algorithm to learn the object detector. This algorithm is to find good features from a confidential map instead of brute-force searching the predefined feature set. The confidential map is computed from the importance re-sampled data. A new feature is created by the linear combination of blocks that are selected from different segmented regions. In addition,...
Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates...
Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will...
State-of-the-art pattern recognition methods have difficulties dealing with problems where the dimension of the output space is large. In this article, we propose a framework based on deep architectures (e. g. deep neural networks) in order to deal with this issue. Deep architectures have proven to be efficient for high dimensional input problems such as image classification, due to their ability...
This paper presents an algorithm for object localization and segmentation. The algorithm uses machine learning, and statistical and combinatorial optimization tools to build a tracker that is robust to noise and occlusions. The method is based on a novel energy formulation and its dual use for object localization and segmentation. The energy uses kernel principal component analysis to incorporate...
Image annotation has been an active research topic in recent years. However, labels are usually associated with images instead of individual regions in the training set, which poses a major challenge for learning strategy. In this paper, we formulate image annotation as a semi-supervised learning problem under multi-instance learning framework. A novel graph based semi-supervised learning approach...
Automatic image annotation is the key to semantic-based image retrieval. In this paper we formulate image annotation as a supervised multi-class labeling problem. The relationship between low-level visual features and semantic concepts is found by supervised Bayesian learning. Color and texture features form two separate vectors, for which two independent Gaussian mixture models (GMM) are estimated...
Considering the conventional defects of boosting cascade, such as overwhelmed training computation, inaccurate threshold adjustment, face detection based on multiple instances and boosting cascade presents a new way. This paper explores the solutions of boosting machine learning and its threshold adjustment strategies, which utilize separated training sets, large scale set with bootstrapping, various...
Level set methods are very useful models in image segmentation, but require delicate adjustments of many parameters, which are typically determined empirically. This paper proposes a novel automatic method to address the challenge of parameter tuning for level set methods. It analyses the energy impact on the objects of interest during construction of the final contours, using a supervised machine...
Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many...
Task learning from observations of non-expert human users will be a core feature of future cognitive robots. However, the problem of task segmentation has only received minor attention. In this paper, we present a new approach to classifying and segmenting series of observations into a set of candidate motions. As basis for these candidates, we use structured UKR manifolds, a modified version of unsupervised...
Change detection based on oriented-object employs objects to show real world. It can reflect visually change of real objects. Result of the method is easier to be understood and re-used. Meanwhile, applying support vector machine (SVM) to change detection can avoid requiring for samples distributing like traditional methods and the questions resulted from over learning like other machine learning...
Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and transductive support vector machine (TSVM) has been presented-RSTSVM-MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every...
In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a...
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