Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

A localized correlation function for stereoscopic image matching

We propose and study a localized correlation function for stereoscopic matching . The latter is based on the wavelet decomposition of the input images . Contrarily to « coarse-to-fine » algorithms, this one simultaneously processes information at the different scales . The localized correlation function is defined by locally integrating with respect to the scale variable . We show that it is equivalent to the definition of a correlation kernel, which is extremely precise in terms of position and disparity . The definition can then be modified in order to account for the local frequency content of the images . Then, we suggest pre-processings of the images : we argue in favour of the use of multiresolution contrast techniques, associated to a quadratic normalization .Nous proposons et étudions une fonction de corrélation localisée permettant la mise en correspondance d'images stéréoscopiques. Celle-ci est fondée sur la décomposition en échelles des images d'entrée. A l'inverse des algorithmes de type « coarse-to-fine », celui-ci traite simultanément les informations des différentes bandes de fréquence. Pour cela, nous définissons en chaque point des deux images une fonction de corrélation localisée par intégration sur le paramètre d'échelle, dont nous montrons qu'elle est équivalente à la définition d'un noyau de corrélation extrêmement fin dans les paramètres de position et de disparité. La définition peut ensuite être modifiée pour prendre en compte la composition fréquentielle locale des images d'entrées. Enfin, nous nous intéressons au problème de la normalisation préalable des images à apparier et justifions le choix du contraste multirésolution associé à une normalisation quadratique

On-Off Intermittency in Time Series of Spontaneous Paroxysmal Activity in Rats with Genetic Absence Epilepsy

Dynamic behavior of complex neuronal ensembles is a topic comprising a streamline of current researches worldwide. In this article we study the behavior manifested by epileptic brain, in the case of spontaneous non-convulsive paroxysmal activity. For this purpose we analyzed archived long-term recording of paroxysmal activity in animals genetically susceptible to absence epilepsy, namely WAG/Rij rats. We first report that the brain activity alternated between normal states and epilepsy paroxysms is the on-off intermittency phenomenon which has been observed and studied earlier in the different nonlinear systems.Comment: 11 pages, 6 figure

Hole weak anti-localization in a strained-Ge surface quantum well

We report a magneto-transport study of a two-dimensional hole gas confined to a strained Ge quantum well grown on a relaxed Si0.2Ge0.8 virtual substrate. The conductivity of the hole gas measured as a function of a perpendicular magnetic field exhibits a zero-field peak resulting from weak anti-localization. The peak develops and becomes stronger upon increasing the hole density by means of a top gate electrode. This behavior is consistent with a Rashba-type spin-orbit coupling whose strength is proportional to the perpendicular electric field and hence to the carrier density. In the low-density, the single-subband regime, by fitting the weak anti-localization peak to an analytic model, we extract the characteristic transport time scales and a spin splitting energy ΔSO∼ΔSO∼ 1 meV. Tight-binding calculations show that ΔSO is dominated by a cubic term in the in-plane wave vector. Finally, we observe a weak anti-localization peak also for magnetic fields parallel to the quantum well and associate this finding to an effect of intersubband scattering induced by interface defects

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept

Time Scale Approach for Chirp Detection

International audienceTwo different approaches for joint detection and estimation of signals embedded in stationary random noise are considered and compared, for the subclass of amplitude and frequency modulated signals. Matched filter approaches are compared to time-frequency and time scale based approaches. Particular attention is paid to the case of the so-called " power-law chirps " , characterized by monomial and polynomial amplitude and frequency functions. As target application, the problem of gravitational waves at interferometric detectors is considered

An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions

International audienceAn error occurs in graph-based keypoint matching when key-points in two different images are matched by an algorithm but do not correspond to the same physical point. Most previous methods acquire keypoints in a black-box manner, and focus on developing better algorithms to match the provided points. However to study the complete performance of a matching system one has to study errors through the whole matching pipeline, from keypoint detection, candidate selection to graph optimisation. We show that in the full pipeline there are six different types of errors that cause mismatches. We then present a matching framework designed to reduce these errors. We achieve this by adapting keypoint detectors to better suit the needs of graph-based matching, and achieve better graph constraints by exploiting more information from their keypoints. Our framework is applicable in general images and can handle clutter and motion discontinuities. We also propose a method to identify many mismatches a posteriori based on Left-Right Consistency inspired by stereo matching due to the asymmetric way we detect keypoints and define the graph