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| Author | Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
| Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 4 | Pages | 1627-1636 |
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| Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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ISE; CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
| Year | 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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LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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| Author | Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Robust Head Gestures Recognition for Assistive Technology | Type | Book Chapter | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume | 8495 | Issue | Pages | 152-161 | |
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| Abstract | This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture. | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-319-07490-0 | Medium | |
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LAMP; | Approved | no | ||
| Call Number | Admin @ si @ TSR2014b | Serial | 2505 | ||
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| Author | Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Saliency for fine-grained object recognition in domains with scarce training data | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 94 | Issue | Pages | 62-73 | |
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| Abstract | This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline. | ||||
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LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FGW2019 | Serial | 3264 | ||
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| Author | Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Leveraging local neighborhood topology for large scale person re-identification | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 12 | Pages | 3767–3778 |
| Keywords | Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 | ||||
| Abstract | In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. | ||||
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LAMP; 601.240; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ KLB2014a | Serial | 2522 | ||
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| Author | Francesco Ciompi; Oriol Pujol; Petia Radeva | ||||
| Title | ECOC-DRF: Discriminative random fields based on error correcting output codes | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 6 | Pages | 2193-2204 |
| Keywords | Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models | ||||
| Abstract | We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. | ||||
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LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ CPR2014b | Serial | 2470 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Approximate polytope ensemble for one-class classification | Type | Journal Article | ||
| Year | 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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MILAB; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ CPR2014a | Serial | 2469 | ||
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| Author | Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva | ||||
| Title | Topic modelling for routine discovery from egocentric photo-streams | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 104 | Issue | Pages | 107330 | |
| Keywords | Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling | ||||
| Abstract | Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed. | ||||
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MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TWP2020 | Serial | 3435 | ||
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| Author | Oriol Pujol; Sergio Escalera; Petia Radeva | ||||
| Title | An Incremental Node Embedding Technique for Error Correcting Output Codes | Type | Journal | ||
| Year | 2008 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 41 | Issue | 2 | Pages | 713–725 |
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MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PER2008 | Serial | 942 | ||
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| Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
| Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 44 | Issue | 10-11 | Pages | 2414-2416 |
| Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
| Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
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| Author | Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia | ||||
| Title | Dense extreme inception network for edge detection | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 139 | Issue | Pages | 109461 | |
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| Abstract | Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. | ||||
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MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SSH2023 | Serial | 3982 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Towards Automatic Polyp Detection with a Polyp Appearance Model | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 9 | Pages | 3166-3182 |
| Keywords | Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot | ||||
| Abstract | This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes |
MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2012; IAM @ iam | Serial | 1997 | ||
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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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OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
| Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 6 | Pages | 2432-2444 |
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| Abstract | IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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OR; MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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| Author | Mario Hernandez; Joao Sanchez; Jordi Vitria | ||||
| Title | Selected papers from Iberian Conference on Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume | 45 | Issue | 9 | Pages | 3047-3582 |
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| ISSN | 0031-3203 | ISBN | Medium | ||
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OR;MV | Approved | no | ||
| Call Number | Admin @ si @ HSV2012 | Serial | 2069 | ||
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