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With the rapid accumulation of high dimensional data, dimensionality reduction plays a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pair wise constraints. In this setting, domain knowledge is given in the form of pair wise constraints, which specifies whether a pair of instances belong to the same class...
Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated...
This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is...
In this paper, a kernelized version of nonparametric discriminant analysis is proposed that we name KNDA. The main idea is to first map the original data into another high-dimensional space, and then to perform nonparametric discriminant analysis in the high dimensional space. Nonparametric discriminant analysis can relax the Gaussian assumption required for the classical linear discriminant analysis,...
A crucial step in many vision based applications, such as localization and structure from motion, is the data association between a large map of known 3D points and 2D features perceived by a new camera. In this paper, we propose a novel approach to predict the visibility of known 3D points with respect to a query camera in large-scale environments. In our approach, we model the visibility of each...
This paper is concerned with switching systems which belong to a special class of Hybrid Dynamical Systems. The objective is to estimate switching times and to recognize the current mode. The general principle of the method is to use indicator signals, named residuals, which are generated by projecting the sensor measures into a subspace related to the input signals on a given time window. The advantage...
Recent approaches of graph comparison consider graphs as sets of paths. Kernels on graphs are then computed from kernels on paths. A common strategy for graph retrieval is to perform pairwise comparisons. In this paper, we propose to follow a different strategy, where we collect a set of paths into a dictionary, and then project each graph to this dictionary. Then, graphs can be classified using powerful...
Outlier detection keeps an important and attractive task of the knowledge discovery in databases. In this paper, a novel approach named Multi-scale Local Kernel Regression is proposed. It transfers the unsupervised learning of outlier detection to the classic non-parameter regression learning. Through preprocessing the original data by the basic local density-based method, it adopts the local kernel...
Automatic object recognition plays a central role in numerous applications, such as image retrieval and robot navigation. A now classical strategy consists to compute a bag of features within a sliding window and to compare this bag with precomputed models. One main drawback of this approach is the use of an unstructured bag of features which do not allow to take into account relationships which may...
A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining task specific datasets of visual categories is, however, far more tedious than obtaining a generic dataset of the same classes. We propose an Incremental Multiple Kernel Learning (IMKL) approach to object recognition that...
Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods...
A kernel PCA-based semantic feature estimation approach for similar image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image. First, our method performs semantic clustering of the database images and derives a new map from a nonlinear eigenspace of visual and semantic features in...
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