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Katerine Diaz, Jesus Martinez del Rincon, Aura Hernandez-Sabate, Marçal Rusiñol, & Francesc J. Ferri. (2018). Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction. JMIV - Journal of Mathematical Imaging and Vision, 60(4), 512–524.
Abstract: This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
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Joan Serrat, Felipe Lumbreras, & Idoia Ruiz. (2018). Learning to measure for preshipment garment sizing. MEASURE - Measurement, 130, 327–339.
Abstract: Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.
Keywords: Apparel; Computer vision; Structured prediction; Regression
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Xavier Soria, Angel Sappa, & Riad I. Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images. SENS - Sensors, 18(7), 2059.
Abstract: Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
Keywords: RGB-NIR sensor; multispectral imaging; deep learning; CNNs
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Katerine Diaz, Jesus Martinez del Rincon, Marçal Rusiñol, & Aura Hernandez-Sabate. (2019). Feature Extraction by Using Dual-Generalized Discriminative Common Vectors. JMIV - Journal of Mathematical Imaging and Vision, 61(3), 331–351.
Abstract: In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.
Keywords: Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning
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Adrien Gaidon, Antonio Lopez, & Florent Perronnin. (2018). The Reasonable Effectiveness of Synthetic Visual Data. IJCV - International Journal of Computer Vision, 126(9), 899–901.
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