Marc Masana, Joost Van de Weijer, & Andrew Bagdanov. (2016). On-the-fly Network pruning for object detection. In International conference on learning representations.
Abstract: Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image.
These bounding boxes are highly correlated since they originate from the same
image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result.
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Egils Avots, M. Daneshmanda, Andres Traumann, Sergio Escalera, & G. Anbarjafaria. (2016). Automatic garment retexturing based on infrared information. CG - Computers & Graphics, 59, 28–38.
Abstract: This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms.
Keywords: Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading
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Katerine Diaz, Aura Hernandez-Sabate, & Antonio Lopez. (2016). A reduced feature set for driver head pose estimation. ASOC - Applied Soft Computing, 45, 98–107.
Abstract: Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.
Keywords: Head pose estimation; driving performance evaluation; subspace based methods; linear regression
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Marc Oliu, Ciprian Corneanu, Laszlo A. Jeni, Jeffrey F. Cohn, Takeo Kanade, & Sergio Escalera. (2016). Continuous Supervised Descent Method for Facial Landmark Localisation. In 13th Asian Conference on Computer Vision (Vol. 10112, pp. 121–135). LNCS.
Abstract: Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.
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Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes Garcıa-Martinez, Fethi Bougares, et al. (2016). Does Multimodality Help Human and Machine for Translation and Image Captioning? In 1st conference on machine translation.
Abstract: This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.
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Esteve Cervantes, Long Long Yu, Andrew Bagdanov, Marc Masana, & Joost Van de Weijer. (2016). Hierarchical Part Detection with Deep Neural Networks. In 23rd IEEE International Conference on Image Processing.
Abstract: Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals.
Keywords: Object Recognition; Part Detection; Convolutional Neural Networks
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Sergio Escalera, Vassilis Athitsos, & Isabelle Guyon. (2016). Challenges in multimodal gesture recognition. JMLR - Journal of Machine Learning Research, 17, 1–54.
Abstract: This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
Keywords: Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM
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Muhammad Anwer Rao, Fahad Shahbaz Khan, Joost Van de Weijer, & Jorma Laaksonen. (2016). Combining Holistic and Part-based Deep Representations for Computational Painting Categorization. In 6th International Conference on Multimedia Retrieval.
Abstract: Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.
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Pejman Rasti, Salma Samiei, Mary Agoyi, Sergio Escalera, & Gholamreza Anbarjafari. (2016). Robust non-blind color video watermarking using QR decomposition and entropy analysis. JVCIR - Journal of Visual Communication and Image Representation, 38, 838–847.
Abstract: Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks.
Keywords: Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition
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Gerard Canal, Sergio Escalera, & Cecilio Angulo. (2016). A Real-time Human-Robot Interaction system based on gestures for assistive scenarios. CVIU - Computer Vision and Image Understanding, 149, 65–77.
Abstract: Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times.
Keywords: Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2016). Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels. In 29th Canadian Conference on Artificial Intelligence (Vol. 9673, pp. 3–14). Springer International Publishing.
Abstract: In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect
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Jun Wan, Yibing Zhao, Shuai Zhou, Isabelle Guyon, & Sergio Escalera. (2016). ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition. In 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops.
Abstract: In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions
on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting
and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented.
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Florin Popescu, Stephane Ayache, Sergio Escalera, Xavier Baro, Cecile Capponi, Patrick Panciatici, et al. (2016). From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning. In European Geosciences Union General Assembly (Vol. 18).
Abstract: The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image.
We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks.
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2016). Support Vector Machines with Time Series Distance Kernels for Action Classification. In IEEE Winter Conference on Applications of Computer Vision (pp. 1–7).
Abstract: Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.
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Debora Gil, Sergio Vera, Agnes Borras, Albert Andaluz, & Miguel Angel Gonzalez Ballester. (2017). Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities. MIA - Medical Image Analysis, 35, 390–402.
Abstract: Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a con dent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies.
Keywords: Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points
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