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In this article we present CoSC, a generic framework for collaborative segmentation and classification. The framework is guided by both radiometric homogeneity based criteria and implicit semantic criteria to segment and extract the objects of a given thematic class. We present a proof-of-concept case-study and show that CoSC is able to reach higher confidence for object classification and results...
In active learning, one aims to acquire labeled samples that are particularly useful for training a classifier. In sequential active learning, this sample selection is done in a one-at-a-time manner where the choice of sample t + 1 may depend on the current state of the classifier and the t labeled data points already available. In their deviation from standard random sampling, current active learning...
Natural gradient descent is a metric aware optimization algorithm which utilizes an underlying Riemannian parameter space, and has successfully improved performance in statistical asymptotic and experimental point of view. In this paper, we investigate the bound property of natural gradient descent in stochastic optimization setting. The bound property is analyzed in both direct and indirect ways...
Graph-based semi-supervised learning has recently come into focus for to its two defining phases: graph construction, which converts the data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. And the label inference is based on the smoothness assumption of semi-supervised learning. In this study, we propose an enhanced label inference...
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different...
Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using “opposition-based learning” (OBL). Two types of the “opposites” have been defined in the literature, namely type-I and type-II. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture...
In this paper, we focus on training a classifier from large-scale data with incompletely assigned labels. In other words, we treat samples with following properties: 1. assigned labels are definitely positive, 2. absent labels are not necessarily negative, and 3. samples are allowed to take more than one label. These properties are frequently found in various kinds of computer vision tasks, including...
Essential image processing and analysis tasks, such as image segmentation, simplification and denoising, can be conducted in a unified way by minimizing the Mumford-Shah (MS) functional. Although seductive, this minimization is in practice difficult because it requires to jointly define a sharp set of contours and a smooth version of the initial image. For this reason, various relaxations of the original...
In the past decade, much progress has been made in image denoising due to the use of low-rank representation and sparse coding. In the meanwhile, state-of-the-art algorithms also rely on an iteration step to boost the denoising performance. However, the boosting step is fixed or non-adaptive. In this work, we perform rank-1 based fixed-point analysis, then, guided by our analysis, we develop the first...
Face recognition with partial occlusion is one of the urgent and challenging problems in the pattern recognition research. Using the Alternating Direction Method of Multipliers (ADMM), the recently proposed nuclear norm based matrix regression model (NMR) has been shown a great potential in dealing with the structural noise. And yet, ADMM needs to bring into an auxiliary variable and only exploits...
In this paper, we propose an algorithm for missing value recovery of visual data such as image or video. These missing values may result from the corruption in acquisition process, or user-specified unexpected outliers. This problem exists in wide range of applications. We use the nuclear norm (NN) regularization to enforce the global consistency of the image, while the total variation (TV) regularization...
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