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Weiqing Min; Shuqiang Jiang; Jitao Sang; Huayang Wang; Xinda Liu; Luis Herranz |
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Title |
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration |
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Journal Article |
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2017 |
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IEEE Transactions on Multimedia |
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TMM |
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19 |
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5 |
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1100 - 1113 |
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This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attribute-enhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., “chicken” and “mushroom”) and nonvisible ingredients (e.g., “salt” and “oil”), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference from food images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach. |
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LAMP; 600.120 |
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Admin @ si @ MJS2017 |
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2964 |
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Luis Herranz; Shuqiang Jiang; Ruihan Xu |
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Title |
Modeling Restaurant Context for Food Recognition |
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Journal Article |
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2017 |
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IEEE Transactions on Multimedia |
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TMM |
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19 |
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2 |
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430 - 440 |
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Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. |
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LAMP; 600.120 |
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Admin @ si @ HJX2017 |
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2965 |
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Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
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Frequency-based Enhancement Network for Efficient Super-Resolution |
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2022 |
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IEEE Access |
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ACCESS |
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10 |
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57383-57397 |
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Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
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Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet |
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18 May 2022 |
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IEEE |
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ISE |
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Admin @ si @ BRF2022a |
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3747 |
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F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Title |
Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos |
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2017 |
Publication |
Machine Vision and Applications |
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MVAP |
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1-20 |
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Specular highlights; bright spot regions segmentation; region classification; colonoscopy |
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A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance dening specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classication
of bright spot regions. The former denes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classication stage performed by a linear SVM classier using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being
closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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MV; 600.096; 600.175 |
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Admin @ si @ SBS2017 |
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2975 |
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Author |
Zhijie Fang; David Vazquez; Antonio Lopez |
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Title |
On-Board Detection of Pedestrian Intentions |
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Journal Article |
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2017 |
Publication |
Sensors |
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SENS |
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17 |
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10 |
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2193 |
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pedestrian intention; ADAS; self-driving |
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Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 |
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Admin @ si @ FVL2017 |
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2983 |
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Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari |
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Title |
Training my car to see using virtual worlds |
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Journal Article |
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2017 |
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Image and Vision Computing |
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IMAVIS |
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38 |
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102-118 |
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Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. |
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ADAS; 600.118 |
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Admin @ si @ LVS2017 |
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2985 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Colour Constancy Beyond the Classical Receptive Field |
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2018 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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40 |
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9 |
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2081 - 2094 |
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The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2018a |
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2990 |
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Arash Akbarinia; C. Alejandro Parraga |
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Feedback and Surround Modulated Boundary Detection |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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12 |
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1367–1380 |
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Boundary detection; Surround modulation; Biologically-inspired vision |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2018b |
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2991 |
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Marçal Rusiñol; J. Chazalon; Katerine Diaz |
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Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
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2018 |
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Multimedia Tools and Applications |
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MTAP |
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77 |
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11 |
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13773-13798 |
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Augmented reality; Document image matching; Educational applications |
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This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
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DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
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Admin @ si @ RCD2018 |
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2996 |
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Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov |
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Review on computer vision techniques in emergency situations |
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2018 |
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Multimedia Tools and Applications |
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MTAP |
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77 |
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13 |
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17069–17107 |
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Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation |
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In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies. |
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LAMP; 600.068; 600.120 |
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Admin @ si @ LWG2018 |
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3041 |
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Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
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Incremental model learning for spectroscopy-based food analysis |
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2017 |
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Chemometrics and Intelligent Laboratory Systems |
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CILS |
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167 |
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123-131 |
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Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
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In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. |
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ADAS; 600.118 |
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Admin @ si @ DGK2017 |
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3002 |
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Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
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Decremental generalized discriminative common vectors applied to images classification |
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2017 |
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Knowledge-Based Systems |
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KBS |
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131 |
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46-57 |
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Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
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In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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ADAS; 600.118; 600.121 |
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Admin @ si @ DMH2017a |
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3003 |
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Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
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Audio-Visual Emotion Recognition in Video Clips |
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2019 |
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IEEE Transactions on Affective Computing |
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TAC |
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10 |
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1 |
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60-75 |
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This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. |
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1 Jan.-March 2019 |
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HUPBA; 602.143; 602.133 |
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Admin @ si @ NMN2017 |
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3011 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad |
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Locality Regularized Group Sparse Coding for Action Recognition |
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2017 |
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Computer Vision and Image Understanding |
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CVIU |
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158 |
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106-114 |
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Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition |
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Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. |
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HuPBA; no proj |
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no |
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Admin @ si @ BGE2017 |
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3014 |
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Author |
Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera |
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Error-Correcting Factorization |
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2018 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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40 |
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2388-2401 |
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Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. |
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0162-8828 |
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HuPBA; no menciona |
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no |
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Admin @ si @ BPT2018 |
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3015 |
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