Anjan Dutta, & Zeynep Akata. (2019). Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval. In 32nd IEEE Conference on Computer Vision and Pattern Recognition (pp. 5089–5098).
Abstract: Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets.
|
Daniel Ponsa, & Antonio Lopez. (2009). Seguimiento Visual de Contornos Computerizado.
|
Ferran Diego, Daniel Ponsa, Joan Serrat, & Antonio Lopez. (2009). Video alignment for automotive applications.
Keywords: video alignment
|
Jose Manuel Alvarez, & Antonio Lopez. (2009). Model-based road detection using shadowless features and on-line learning.
|
Xavier Boix, Josep M. Gonfaus, Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, Marco Pedersoli, et al. (2009). Combining local and global bag-of-word representations for semantic segmentation. In Workshop on The PASCAL Visual Object Classes Challenge.
|
Eduard Vazquez, Theo Gevers, M. Lucassen, Joost Van de Weijer, & Ramon Baldrich. (2010). Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception. JOSA A - Journal of the Optical Society of America A, 27(3), 613–621.
Abstract: In this paper, computational methods are proposed to compute color edge saliency based on the information content of color edges. The computational methods are evaluated on bottom-up saliency in a psychophysical experiment, and on a more complex task of salient object detection in real-world images. The psychophysical experiment demonstrates the relevance of using information theory as a saliency processing model and that the proposed methods are significantly better in predicting color saliency (with a human-method correspondence up to 74.75% and an observer agreement of 86.8%) than state-of-the-art models. Furthermore, results from salient object detection confirm that an early fusion of color and contrast provide accurate performance to compute visual saliency with a hit rate up to 95.2%.
|
Sergio Escalera, Oriol Pujol, & Petia Radeva. (2010). On the Decoding Process in Ternary Error-Correcting Output Codes. TPAMI - IEEE on Pattern Analysis and Machine Intelligence, 32(1), 120–134.
Abstract: A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved.
|
Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2010). An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes. IMAVIS - Image and Vision Computing, 28(1), 164–176.
Abstract: Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.
|
Fernando Vilariño, Panagiota Spyridonos, Fosca De Iorio, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2010). Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions. TMI - IEEE Transactions on Medical Imaging, 29(2), 246–259.
Abstract: Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions.
|
Mario Rojas, David Masip, A. Todorov, & Jordi Vitria. (2010). Automatic Point-based Facial Trait Judgments Evaluation. In 23rd IEEE Conference on Computer Vision and Pattern Recognition (2715–2720).
Abstract: Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits.
|
Sergio Escalera, Oriol Pujol, Petia Radeva, Jordi Vitria, & Maria Teresa Anguera. (2010). Automatic Detection of Dominance and Expected Interest. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 12.
Abstract: Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
|
O. Fors, J. Nuñez, Xavier Otazu, A. Prades, & Robert D. Cardinal. (2010). Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques. SENS - Sensors, 10(3), 1743–1752.
Abstract: Abstract: In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double the quantum efficiency of the used image sensor or to increase the effective telescope aperture by more than 30% without decreasing the astrometric precision or introducing artificial bias. In the case of orbital objects, the deconvolution technique can double the signal-to-noise ratio of the image, which helps to discover and control dangerous objects as space debris or lost satellites. The benefits obtained using CCD detectors can be extrapolated to any kind of image sensors.
Keywords: image processing; image deconvolution; faint stars; space debris; wavelet transform
|
Sergio Escalera, Oriol Pujol, & Petia Radeva. (2010). Error-Correcting Output Codes Library. JMLR - Journal of Machine Learning Research, 11, 661–664.
Abstract: (Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
|
David Rotger, Petia Radeva, & N. Bruining. (2010). Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images using a Cascade of Classifiers. TITB - IEEE Transactions on Information Technology in Biomedicine, 14(2), 535 – 537.
Abstract: Bioabsorbable drug-eluting coronary stents present a very promising improvement to the common metallic ones solving some of the most important problems of stent implantation: the late restenosis. These stents made of poly-L-lactic acid cause a very subtle acoustic shadow (compared to the metallic ones) making difficult the automatic detection and measurements in images. In this paper, we propose a novel approach based on a cascade of GentleBoost classifiers to detect the stent struts using structural features to code the information of the different subregions of the struts. A stochastic gradient descent method is applied to optimize the overall performance of the detector. Validation results of struts detection are very encouraging with an average F-measure of 81%.
|
Partha Pratim Roy, Umapada Pal, & Josep Llados. (2010). Seal Object Detection in Document Images using GHT of Local Component Shapes. In 10th ACM Symposium On Applied Computing (23–27).
Abstract: Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents.
|