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| Author | Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal | ||||
| Title | Product graph-based higher order contextual similarities for inexact subgraph matching | Type | Journal Article | ||
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2018 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 76 | Issue | Pages | 596-611 | |
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| Abstract | Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. | ||||
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| Notes | DAG; 602.167; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DLB2018 | Serial | 3083 | ||
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| Author | Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu | ||||
| Title | Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video | Type | Journal Article | ||
| Year |
2018 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 80 | Issue | Pages | 64-82 | |
| Keywords | Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition | ||||
| Abstract | Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. | ||||
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RSJ2018 | Serial | 3096 | ||
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| Author | Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler | ||||
| Title | Articulated Motion and Deformable Objects | Type | Journal Article | ||
| Year |
2018 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 79 | Issue | Pages | 55-64 | |
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| Abstract | This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ WEP2018 | Serial | 3126 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild | Type | Journal Article | ||
| Year |
2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 70 | Issue | Pages | 60-74 | |
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| Abstract | Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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| Notes | DAG; 600.084; 601.197; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GoK2017 | Serial | 2886 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas | ||||
| Title | Improving patch‐based scene text script identification with ensembles of conjoined networks | Type | Journal Article | ||
| Year |
2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 67 | Issue | Pages | 85-96 | |
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| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GNK2017 | Serial | 2887 | ||
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| Author | Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Age and gender recognition in the wild with deep attention | Type | Journal Article | ||
| Year |
2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 72 | Issue | Pages | 563-571 | |
| Keywords | Age recognition; Gender recognition; Deep neural networks; Attention mechanisms | ||||
| Abstract | Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. | ||||
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| Notes | ISE; 600.098; 602.133; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RCG2017b | Serial | 2962 | ||
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| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title | Efficient segmentation-free keyword spotting in historical document collections | Type | Journal Article | ||
| Year |
2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 48 | Issue | 2 | Pages | 545–555 |
| Keywords | Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization | ||||
| Abstract | In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. | ||||
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| Notes | DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 | Approved | no | ||
| Call Number | Admin @ si @ RAT2015a | Serial | 2544 | ||
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| Author | Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu | ||||
| Title | Combining where and what in change detection for unsupervised foreground learning in surveillance | Type | Journal Article | ||
| Year |
2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 48 | Issue | 3 | Pages | 709-719 |
| Keywords | Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance | ||||
| Abstract | Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. | ||||
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| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ HPG2015 | Serial | 2589 | ||
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| Author | Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca | ||||
| Title | A coarse-to-fine approach for fast deformable object detection | Type | Journal Article | ||
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2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 48 | Issue | 5 | Pages | 1844-1853 |
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| Abstract | We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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| Notes | ISE; 600.078; 602.005; 605.001; 302.012 | Approved | no | ||
| Call Number | Admin @ si @ PVG2015 | Serial | 2628 | ||
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| Author | Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre | ||||
| Title | Continuous Generalized Procrustes Analysis | Type | Journal Article | ||
| Year |
2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 2 | Pages | 659–671 |
| Keywords | Procrustes analysis; 2D shape model; Continuous approach | ||||
| Abstract | PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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| Notes | OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
| Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title | Boosting the Handwritten Word Spotting Experience by Including the User in the Loop | Type | Journal Article | ||
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2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 3 | Pages | 1063–1072 |
| Keywords | Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling | ||||
| Abstract | In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.045; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RuL2013 | Serial | 2343 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
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2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 1 | Pages | 480-492 |
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| Abstract | Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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| Author | Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke | ||||
| Title | Keyword spotting for self-training of BLSTM NN based handwriting recognition systems | Type | Journal Article | ||
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2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 3 | Pages | 1073-1082 |
| Keywords | Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning | ||||
| Abstract | The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. | ||||
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| Notes | DAG; 600.077; 602.101 | Approved | no | ||
| Call Number | Admin @ si @ FFB2014 | Serial | 2297 | ||
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| Author | Miguel Angel Bautista; Sergio Escalera; Oriol Pujol | ||||
| Title | On the Design of an ECOC-Compliant Genetic Algorithm | Type | Journal Article | ||
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2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 2 | Pages | 865-884 |
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| Abstract | Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BEP2013 | Serial | 2254 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Approximate polytope ensemble for one-class classification | Type | Journal Article | ||
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2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 2 | Pages | 854-864 |
| Keywords | One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning | ||||
| Abstract | In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. | ||||
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| Notes | MILAB; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ CPR2014a | Serial | 2469 | ||
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