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Author | Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund | ||||
Title ![]() |
Back-dropout Transfer Learning for Action Recognition | Type | Journal Article | ||
Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
Volume | 12 | Issue | 4 | Pages | 484-491 |
Keywords | Learning (artificial intelligence); Pattern Recognition | ||||
Abstract | Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ RKM2018 | Serial | 3071 | ||
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Author | Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli | ||||
Title ![]() |
Batch-based activity recognition from egocentric photo-streams revisited | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 21 | Issue | 4 | Pages | 953–965 |
Keywords | Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks | ||||
Abstract | Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ CMR2018 | Serial | 3186 | ||
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Author | Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga | ||||
Title ![]() |
Beyond Eleven Color Names for Image Understanding | Type | Journal Article | ||
Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 29 | Issue | 2 | Pages | 361-373 |
Keywords | Color name; Discriminative descriptors; Image classification; Re-identification; Tracking | ||||
Abstract | Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. | ||||
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Notes | LAMP; NEUROBIT; 600.068; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YYW2018 | Serial | 3087 | ||
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Author | Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez | ||||
Title ![]() |
Beyond Oneshot Encoding: lower dimensional target embedding | Type | Journal Article | ||
Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 75 | Issue | Pages | 21-31 | |
Keywords | Error correcting output codes; Output embeddings; Deep learning; Computer vision | ||||
Abstract | Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. | ||||
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Notes | ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RBE2018 | Serial | 3120 | ||
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Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen | ||||
Title ![]() |
Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification | Type | Journal Article | ||
Year | 2018 | Publication | ISPRS Journal of Photogrammetry and Remote Sensing | Abbreviated Journal | ISPRS J |
Volume | 138 | Issue | Pages | 74-85 | |
Keywords | Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis | ||||
Abstract | Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene | ||||
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Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ RKW2018 | Serial | 3158 | ||
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Author | Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
Title ![]() |
BronchoX: bronchoscopy exploration software for biopsy intervention planning | Type | Journal | ||
Year | 2018 | Publication | Healthcare Technology Letters | Abbreviated Journal | HTL |
Volume | 5 | Issue | 5 | Pages | 177–182 |
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Abstract | Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea. | ||||
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Notes | IAM; 600.096; 600.075; 601.323; 601.337; 600.145 | Approved | no | ||
Call Number | Admin @ si @ RSB2018a | Serial | 3132 | ||
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Author | Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas | ||||
Title ![]() |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets | Type | Conference Article | ||
Year | 2018 | Publication | International Workshop on Artificial Intelligence and Pattern Recognition | Abbreviated Journal | |
Volume | 11047 | Issue | Pages | 34-42 | |
Keywords | Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis | ||||
Abstract | The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. | ||||
Address | Cuba; September 2018 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | IWAIPR | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ BGÑ2018 | Serial | 3237 | ||
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Author | Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri | ||||
Title ![]() |
Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. | Type | Journal Article | ||
Year | 2018 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 101 | Issue | Pages | 184-198 | |
Keywords | Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification | ||||
Abstract | Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. Recent finding Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ BLA2018 | Serial | 3188 | ||
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Author | Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title ![]() |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification | Type | Conference Article | ||
Year | 2018 | Publication | 8th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
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Abstract | Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. | ||||
Address | Xian; China; May 2018 | ||||
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Area | Expedition | Conference | FGW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ HKI2018 | Serial | 3118 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title ![]() |
Color encoding in biologically-inspired convolutional neural networks | Type | Journal Article | ||
Year | 2018 | Publication | Vision Research | Abbreviated Journal | VR |
Volume | 151 | Issue | Pages | 7-17 | |
Keywords | Color coding; Computer vision; Deep learning; Convolutional neural networks | ||||
Abstract | Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. | ||||
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Notes | CIC; 600.051; 600.087 | Approved | no | ||
Call Number | Admin @ si @RaV2018 | Serial | 3114 | ||
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Author | Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa | ||||
Title ![]() |
Color Naming for Multi-Color Fashion Items | Type | Conference Article | ||
Year | 2018 | Publication | 6th World Conference on Information Systems and Technologies | Abbreviated Journal | |
Volume | 747 | Issue | Pages | 64-73 | |
Keywords | Deep learning; Color; Multi-label | ||||
Abstract | There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. | ||||
Address | Naples; March 2018 | ||||
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Area | Expedition | Conference | WORLDCIST | ||
Notes | LAMP; 600.109; 601.309; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWR2018 | Serial | 3161 | ||
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Author | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell | ||||
Title ![]() |
Color-based data augmentation for Reflectance Estimation | Type | Conference Article | ||
Year | 2018 | Publication | 26th Color Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 284-289 | ||
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Abstract | Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. | ||||
Address | Vancouver; November 2018 | ||||
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Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ SSB2018a | Serial | 3129 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title ![]() |
Colour Constancy Beyond the Classical Receptive Field | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 40 | Issue | 9 | Pages | 2081 - 2094 |
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Abstract | 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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Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2018a | Serial | 2990 | ||
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Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana | ||||
Title ![]() |
Context Proposals for Saliency Detection | Type | Journal Article | ||
Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 174 | Issue | Pages | 1-11 | |
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Abstract | One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated. In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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Notes | LAMP; 600.109; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ AWD2018 | Serial | 3241 | ||
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Author | Aymen Azaza | ||||
Title ![]() |
Context, Motion and Semantic Information for Computational Saliency | Type | Book Whole | ||
Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of explicit context modelling for saliency estimation. Several important works in saliency are based on the usage of object proposals. However, these methods focus on the saliency of the object proposal itself and ignore the context. To introduce context in such saliency approaches, we couple every object proposal with its direct context. This allows us to evaluate the importance of the immediate surround (context) for its saliency. We propose several saliency features which are computed from the context proposals including features based on omni-directional and horizontal context continuity. Secondly, we investigate the usage of top-downmethods (high-level semantic information) for the task of saliency prediction since most computational methods are bottom-up or only include few semantic classes. We propose to consider a wider group of object classes. These objects represent important semantic information which we will exploit in our saliency prediction approach. Thirdly, we develop a method to detect video saliency by computing saliency from supervoxels and optical flow. In addition, we apply the context features developed in this thesis for video saliency detection. The method combines shape and motion features with our proposed context features. To summarize, we prove that extending object proposals with their direct context improves the task of saliency detection in both image and video data. Also the importance of the semantic information in saliency estimation is evaluated. Finally, we propose a newmotion feature to detect saliency in video data. The three proposed novelties are evaluated on standard saliency benchmark datasets and are shown to improve with respect to state-of-the-art. |
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Address | October 2018 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ali Douik | |
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ISSN | ISBN | 978-84-945373-9-4 | Medium | ||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ Aza2018 | Serial | 3218 | ||
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