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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | Combining Local and Global Learners in the Pairwise Multiclass Classification | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
| Volume | 18 | Issue | 4 | Pages | 845-860 |
| Keywords | Multiclass classification; Pairwise approach; One-versus-one | ||||
| Abstract | Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. | ||||
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| Publisher | Springer London | Place of Publication | Editor | ||
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| ISSN | 1433-7541 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2014 | Serial | 2441 | ||
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| Author | Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez | ||||
| Title | Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Systems, Man and Cybernetics (Part B) | Abbreviated Journal | TSMCB |
| Volume | 44 | Issue | 12 | Pages | 2379-2390 |
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| Abstract | The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
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| Notes | HuPBA; ISE; 600.078;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LRE2014 | Serial | 2442 | ||
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| Author | Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez | ||||
| Title | A survey on model based approaches for 2D and 3D visual human pose recovery | Type | Journal Article | ||
| Year | 2014 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 14 | Issue | 3 | Pages | 4189-4210 |
| Keywords | human pose recovery; human body modelling; behavior analysis; computer vision | ||||
| Abstract | Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. | ||||
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| Notes | HuPBA; ISE; 600.046; 600.063; 600.078;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PEA2014 | Serial | 2443 | ||
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| Author | Frederic Sampedro; Anna Domenech; Sergio Escalera | ||||
| Title | Obtaining quantitative global tumoral state indicators based on whole-body PET/CT scans: A breast cancer case study | Type | Journal Article | ||
| Year | 2014 | Publication | Nuclear Medicine Communications | Abbreviated Journal | NMC |
| Volume | 35 | Issue | 4 | Pages | 362-371 |
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| Abstract | Objectives: In this work we address the need for the computation of quantitative global tumoral state indicators from oncological whole-body PET/computed tomography scans. The combination of such indicators with other oncological information such as tumor markers or biopsy results would prove useful in oncological decision-making scenarios.
Materials and methods: From an ordering of 100 breast cancer patients on the basis of oncological state through visual analysis by a consensus of nuclear medicine specialists, a set of numerical indicators computed from image analysis of the PET/computed tomography scan is presented, which attempts to summarize a patient’s oncological state in a quantitative manner taking into consideration the total tumor volume, aggressiveness, and spread. Results: Results obtained by comparative analysis of the proposed indicators with respect to the experts’ evaluation show up to 87% Pearson’s correlation coefficient when providing expert-guided PET metabolic tumor volume segmentation and 64% correlation when using completely automatic image analysis techniques. Conclusion: Global quantitative tumor information obtained by whole-body PET/CT image analysis can prove useful in clinical nuclear medicine settings and oncological decision-making scenarios. The completely automatic computation of such indicators would improve its impact as time efficiency and specialist independence would be achieved. |
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | SDE2014a | Serial | 2444 | ||
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| Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou | ||||
| Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 23 | Issue | 1 | Pages | 83-95 |
| Keywords | color constancy; CRF; multi-illuminant | ||||
| Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ BRW2014 | Serial | 2451 | ||
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| Author | David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson | ||||
| Title | Automated Prediction of Preferences Using Facial Expressions | Type | Journal Article | ||
| Year | 2014 | Publication | PloS one | Abbreviated Journal | Plos |
| Volume | 9 | Issue | 2 | Pages | e87434 |
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| Abstract | We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MNT2014 | Serial | 2453 | ||
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| Author | David Fernandez; Josep Llados; Alicia Fornes | ||||
| Title | A graph-based approach for segmenting touching lines in historical handwritten documents | Type | Journal Article | ||
| Year | 2014 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 17 | Issue | 3 | Pages | 293-312 |
| Keywords | Text line segmentation; Handwritten documents; Document image processing; Historical document analysis | ||||
| Abstract | Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 1433-2833 | ISBN | Medium | ||
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| Notes | DAG; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ FLF2014 | Serial | 2459 | ||
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| Author | Carlo Gatta; Francesco Ciompi | ||||
| Title | Stacked Sequential Scale-Space Taylor Context | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 8 | Pages | 1694-1700 |
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| Abstract | We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ GaC2014 | Serial | 2466 | ||
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| Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
| Title | Context-aware features and robust image representations | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 25 | Issue | 2 | Pages | 339-348 |
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| Abstract | Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. | ||||
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| Notes | LAMP; 600.079;MILAB | Approved | no | ||
| Call Number | Admin @ si @ MCG2014 | Serial | 2467 | ||
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| Author | Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta | ||||
| Title | Diaphragm border detection in coronary X-ray angiographies: New method and applications | Type | Journal Article | ||
| Year | 2014 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 38 | Issue | 4 | Pages | 296-305 |
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| Abstract | X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. | ||||
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| Notes | MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ PCR2014 | Serial | 2468 | ||
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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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| Notes | MILAB; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ CPR2014a | Serial | 2469 | ||
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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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| Notes | 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 | Naveen Onkarappa; Angel Sappa | ||||
| Title | Synthetic sequences and ground-truth flow field generation for algorithm validation | Type | Journal Article | ||
| Year | 2015 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 74 | Issue | 9 | Pages | 3121-3135 |
| Keywords | Ground-truth optical flow; Synthetic sequence; Algorithm validation | ||||
| Abstract | Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 1380-7501 | ISBN | Medium | ||
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| Notes | ADAS; 600.055; 601.215; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OnS2014b | Serial | 2472 | ||
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| Author | Monica Piñol; Angel Sappa; Ricardo Toledo | ||||
| Title | Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy | Type | Journal Article | ||
| Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 150 | Issue | A | Pages | 106–115 |
| Keywords | Reinforcement learning; Q-learning; Bag of features; Descriptors | ||||
| Abstract | This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. | ||||
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| Notes | ADAS; 600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ PST2015 | Serial | 2473 | ||
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| Author | P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa | ||||
| Title | Feature Point Descriptors: Infrared and Visible Spectra | Type | Journal Article | ||
| Year | 2014 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 14 | Issue | 2 | Pages | 3690-3701 |
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| Abstract | This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. | ||||
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| Notes | ADAS;600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ RCA2014a | Serial | 2474 | ||
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