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Author | Anjan Dutta; Hichem Sahbi | ||||
Title | Stochastic Graphlet Embedding | Type ![]() |
Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
Volume | Issue | Pages | 1-14 | ||
Keywords | Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality | ||||
Abstract | Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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Notes | DAG; 602.167; 602.168; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DuS2018 | Serial | 3225 | ||
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Author | Xim Cerda-Company; Xavier Otazu | ||||
Title | Color induction in equiluminant flashed stimuli | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
Volume | 36 | Issue | 1 | Pages | 22-31 |
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Abstract | Color induction is the influence of the surrounding color (inducer) on the perceived color of a central region. There are two different types of color induction: color contrast (the color of the central region shifts away from that of the inducer) and color assimilation (the color shifts towards the color of the inducer). Several studies on these effects have used uniform and striped surrounds, reporting color contrast and color assimilation, respectively. Other authors [J. Vis. 12(1), 22 (2012) [CrossRef] ] have studied color induction using flashed uniform surrounds, reporting that the contrast is higher for shorter flash duration. Extending their study, we present new psychophysical results using both flashed and static (i.e., non-flashed) equiluminant stimuli for both striped and uniform surrounds. Similarly to them, for uniform surround stimuli we observed color contrast, but we did not obtain the maximum contrast for the shortest (10 ms) flashed stimuli, but for 40 ms. We only observed this maximum contrast for red, green, and lime inducers, while for a purple inducer we obtained an asymptotic profile along the flash duration. For striped stimuli, we observed color assimilation only for the static (infinite flash duration) red–green surround inducers (red first inducer, green second inducer). For the other inducers’ configurations, we observed color contrast or no induction. Since other studies showed that non-equiluminant striped static stimuli induce color assimilation, our results also suggest that luminance differences could be a key factor to induce it. | ||||
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Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
Call Number | Admin @ si @ CeO2019 | Serial | 3226 | ||
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Author | Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan | ||||
Title | Synthetic Data Generation for End-to-End Thermal Infrared Tracking | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 28 | Issue | 4 | Pages | 1837 - 1850 |
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Abstract | The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers. | ||||
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Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YGW2019 | Serial | 3228 | ||
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Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
Title | Assessment of intracoronary stent location and extension in intravascular ultrasound sequences | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | Medical Physics | Abbreviated Journal | MEDPHYS |
Volume | 46 | Issue | 2 | Pages | 484-493 |
Keywords | IVUS; malapposition; stent; ultrasound | ||||
Abstract | PURPOSE:
An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. METHODS: The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. RESULTS: The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. CONCLUSIONS: Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method. |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ BCR2019 | Serial | 3231 | ||
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Author | Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva | ||||
Title | Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants | Type ![]() |
Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | |
Volume | 20 | Issue | 12 | Pages | 3266 - 3275 |
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Abstract | The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ ARB2018 | Serial | 3236 | ||
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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 | Hans Stadthagen-Gonzalez; M. Carmen Parafita; C. Alejandro Parraga; Markus F. Damian | ||||
Title | Testing alternative theoretical accounts of code-switching: Insights from comparative judgments of adjective noun order | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | International journal of bilingualism: interdisciplinary studies of multilingual behaviour | Abbreviated Journal | IJB |
Volume | 23 | Issue | 1 | Pages | 200-220 |
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Abstract | Objectives:
Spanish and English contrast in adjective–noun word order: for example, brown dress (English) vs. vestido marrón (‘dress brown’, Spanish). According to the Matrix Language model (MLF) word order in code-switched sentences must be compatible with the word order of the matrix language, but working within the minimalist program (MP), Cantone and MacSwan arrived at the descriptive generalization that the position of the noun phrase relative to the adjective is determined by the adjective’s language. Our aim is to evaluate the predictions derived from these two models regarding adjective–noun order in Spanish–English code-switched sentences. Methodology: We contrasted the predictions from both models regarding the acceptability of code-switched sentences with different adjective–noun orders that were compatible with the MP, the MLF, both, or none. Acceptability was assessed in Experiment 1 with a 5-point Likert and in Experiment 2 with a 2-Alternative Forced Choice (2AFC) task. |
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ SPP2019 | Serial | 3242 | ||
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Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca | ||||
Title | Camera pose estimation in multi-view environments: From virtual scenarios to the real world | Type ![]() |
Journal Article | ||
Year | 2021 | Publication | Image and Vision Computing | Abbreviated Journal | IVC |
Volume | 110 | Issue | Pages | 104182 | |
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Abstract | This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. | ||||
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Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ CSV2021 | Serial | 3577 | ||
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Author | Xinhang Song; Shuqiang Jiang; Luis Herranz; Chengpeng Chen | ||||
Title | Learning Effective RGB-D Representations for Scene Recognition | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 28 | Issue | 2 | Pages | 980-993 |
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Abstract | Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition. | ||||
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Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SJH2019 | Serial | 3247 | ||
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Author | Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone | ||||
Title | DSD: document sparse-based denoising algorithm | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 22 | Issue | 1 | Pages | 177–186 |
Keywords | Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models | ||||
Abstract | In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. | ||||
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Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DRT2019 | Serial | 3254 | ||
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Author | Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan | ||||
Title | Script independent approach for multi-oriented text detection in scene image | Type ![]() |
Journal Article | ||
Year | 2017 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 242 | Issue | Pages | 96-112 | |
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Abstract | Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability. | ||||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DSR2017 | Serial | 3260 | ||
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Author | Mikhail Mozerov; Fei Yang; Joost Van de Weijer | ||||
Title | Sparse Data Interpolation Using the Geodesic Distance Affinity Space | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | IEEE Signal Processing Letters | Abbreviated Journal | SPL |
Volume | 26 | Issue | 6 | Pages | 943 - 947 |
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Abstract | In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MYW2019 | Serial | 3261 | ||
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Author | Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu | ||||
Title | Saliency for fine-grained object recognition in domains with scarce training data | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 94 | Issue | Pages | 62-73 | |
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Abstract | This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline. | ||||
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Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ FGW2019 | Serial | 3264 | ||
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Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
Title | Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 41 | Issue | 8 | Pages | 1862-1878 |
Keywords | Task analysis;Training;Image quality;Visualization;Uncertainty;Labeling;Neural networks;Learning from rankings;image quality assessment;crowd counting;active learning | ||||
Abstract | For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent. | ||||
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Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
Call Number | LWB2019 | Serial | 3267 | ||
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Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo | ||||
Title | Psychophysical evaluation of individual low-level feature influences on visual attention | Type ![]() |
Journal Article | ||
Year | 2019 | Publication | Vision Research | Abbreviated Journal | VR |
Volume | 154 | Issue | Pages | 60-79 | |
Keywords | Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset | ||||
Abstract | In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. | ||||
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Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
Call Number | Admin @ si @ BFO2019a | Serial | 3274 | ||
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