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In this paper a new variation of Support Vector Machines (SVM) is introduced. The proposed method is called Subclass Support Vector Machine (SSVM) and makes use of principles from Discriminant Analysis field using subclasses. The major difference over SVM is that it takes into account the existence of subclasses in the classes and tries to minimize the distribution of the samples within each subclass...
Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space. However, there are many MIL problems that do not fit this formulation well, and hence cause traditional MIL algorithms, which focus...
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned...
This paper describes an active transfer learning technique for multi-view head-pose classification. We combine transfer learning with active learning, where an active learner asks the domain expert to label the few most informative target samples for transfer learning. Employing adaptive multiple-kernel learning for head-pose classification from four low-resolution views, we show how active sampling...
An appropriate dimension reduction of raw data helps to reduce computational time and to reveal the intrinsic structure of complex data. In this paper, a dimension reduction method for regression is proposed. The method is based on the well-known sliced inverse regression and conditional entropy minimization. Using entropy as a measure of dispersion of data distribution, dimension reduction subspace...
Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to...
Traditional nonlinear feature selection methods map the data from an original space into a kernel space to make the data be separated more easily, then move back to the original feature space to select features. However, the performance of clustering or classification is better in the kernel space, so we are able to select the features directly in the kernel space and get the direct importance of...
Insect species recognition is more difficult than generic object recognition because of the similarity between different species. In this paper, we propose a hybrid approach called discriminative local soft coding (DLSoft) which combines local and discriminative coding strategies together. Our method takes use of neighbor codewords to get a local soft coding and class specific codebooks (sets of codewords)...
The large amount of digital data requests for scalable tools like efficient clustering algorithms. Many algorithms for large data sets request linear separability in an Euclidean space. Kernel approaches can capture the non-linear structure but do not scale well for large data sets. Alternatively, data are often represented implicitly by dissimilarities like for protein sequences, whose methods also...
During training and classification, instances are drawn from the instance space and mapped to the feature space. We focus on the problem of detecting hidden changes in the functions that map instances to feature vectors during classification. We call such changes feature shift and introduce an on-line method for detecting it. Our method is based on a robust similarity measure that uses one-class SVM...
The locality and sparsity constrained encoding methods have shown the good image classification performance in recent papers. Among these methods, the common strategy is encoding one descriptor into one code by a learned codebook and then applying SPM and Pooling strategy to get the final image representation. However, the ignorance of local spatial context has been a barrier to improve their discriminative...
Over the recent years, low-level visual descriptors, among which the most popular is the histogram of oriented gradients (HOG), have shown excellent performance in object detection and categorization. We form a hypothesis that the low-level image descriptors can be improved by learning the statistically relevant edge structures from natural images. We validate this hypothesis by introducing a new...
The l1 minimization problem (Lasso) is a basic and critical problem in sparse representation and its applications. Among the solutions, Homotopy is an efficient and effective algorithm. In this paper, we propose a novel kernel algorithm based on Homotopy (KHomotopy) to solve the Lasso problem in kernel space. Then we integrate it in the well known Sparse Representation based Classification (SRC) framework...
This paper advocates a new paradigm, called bag-of-feature-graphs (BoFG), for non-rigid shape retrieval. It represents a shape by constructing graphs among its features, which significantly reduces the number of points involved in computation. Given a vocabulary of geometric words, for each word the BoFG builds a graph that records spatial information of features, weighted by their similarities to...
Clustering often benefits from side information. In this paper, we consider the problem of multi-way constrained spectral clustering with pairwise constraints which encode whether two nodes belong to the same cluster or not. Due to the nontransitive property of cannot-link constraints, it is hard to incorporate cannot-link constraints into the framework. We settle this difficulty by restricting the...
This paper presents a novel analysis and application of the eigensystem of the edge-based Laplacian of a graph. The advantage of using the edge-based Lapla-cian over its vertex-based counterpart is that it significantly expands the set of differential operators that can be implemented in the graph domain. We use the analysis to develop a novel method for defining poseinvariant signatures for non-rigid...
While the skeleton of a 2D shape corresponds to a planar graph, its encoding by usual graph data structures does not allow to capture its planar properties. Graph kernels may be defined on graph's encoding of the skeleton in order to define a similarity measure between shapes. Such graph kernels are usually based on a decomposition of graphs into bags of walks or trails. These linear patterns do not...
We address the problem of featureless pattern recognition under the assumption that pair-wise comparison of objects is arbitrarily scored by real numbers. Such a linear embedding is much more general than the traditional kernel-based approach, which demands positive semi-definiteness of the matrix of object comparisons. This demand is frequently prohibitive and is further complicated if there exist...
Font can be used as a notion of similarity amongst multiple documents written in same script. We could automatically retrieve document images with specific font from a huge digital document repository. So Optical Font Recognition could be a useful pre-processing step in an automated questioned document analysis system for sorting documents with similar fonts. We propose a scheme to identify 10 different...
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...
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