| Records |
| Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma |
Title  |
Scale coding bag of deep features for human attribute and action recognition |
Type |
Journal Article |
| Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
29 |
Issue |
1 |
Pages |
55-71 |
| Keywords |
Action recognition; Attribute recognition; Bag of deep features |
| Abstract |
Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. |
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LAMP; 600.068; 600.079; 600.106; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ KWR2018 |
Serial |
3107 |
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| Author |
H. Chouaib; Salvatore Tabbone; Oriol Ramos Terrades; F. Cloppet; N. Vincent; A.T. Thierry Paquet |
| Title |
Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost |
Type |
Conference Article |
| Year |
2008 |
Publication |
Colloque International Francophone sur l'Ecrit et le Document |
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Pages |
181-186 |
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Rouen, France |
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CIFED |
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DAG |
Approved |
no |
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Admin @ si @ CTR2008 |
Serial |
1874 |
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| Author |
A. Pujol; A.F. Sole; Daniel Ponsa; X. Varona; Juan J. Villanueva |
| Title |
Satellite Image Segmentation Trough Rotational Invariant Feature Eigenvector Projection. |
Type |
Miscellaneous |
| Year |
1999 |
Publication |
Machine Vision and Advanced Image Processing in Remote Sensing, Springer, 317–327. |
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ADAS |
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no |
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ADAS @ adas @ PSP1999 |
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36 |
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| Author |
Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
SASE: RGB-Depth Database for Human Head Pose Estimation |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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Slides |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
| Notes |
HuPBA;MILAB; |
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no |
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Admin @ si @ LEA2016a |
Serial |
2840 |
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| Author |
Eduard Vazquez; Theo Gevers; M. Lucassen; Joost Van de Weijer; Ramon Baldrich |
| Title |
Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception |
Type |
Journal Article |
| Year |
2010 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
| Volume |
27 |
Issue |
3 |
Pages |
613–621 |
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| Abstract |
In this paper, computational methods are proposed to compute color edge saliency based on the information content of color edges. The computational methods are evaluated on bottom-up saliency in a psychophysical experiment, and on a more complex task of salient object detection in real-world images. The psychophysical experiment demonstrates the relevance of using information theory as a saliency processing model and that the proposed methods are significantly better in predicting color saliency (with a human-method correspondence up to 74.75% and an observer agreement of 86.8%) than state-of-the-art models. Furthermore, results from salient object detection confirm that an early fusion of color and contrast provide accurate performance to compute visual saliency with a hit rate up to 95.2%. |
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ISE;CIC |
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no |
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CAT @ cat @ VGL2010 |
Serial |
1275 |
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| Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana |
| Title |
Saliency from High-Level Semantic Image Features |
Type |
Journal |
| Year |
2020 |
Publication |
SN Computer Science |
Abbreviated Journal |
SN |
| Volume |
1 |
Issue |
4 |
Pages |
1-12 |
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| Abstract |
Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). |
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| Notes |
LAMP; 600.120; 600.109; 600.106 |
Approved |
no |
| Call Number |
Admin @ si @ AWD2020 |
Serial |
3503 |
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| Author |
Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu |
| Title |
Saliency for free: Saliency prediction as a side-effect of object recognition |
Type |
Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
150 |
Issue |
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Pages |
1-7 |
| Keywords |
Saliency maps; Unsupervised learning; Object recognition |
| Abstract |
Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. |
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| Notes |
LAMP; 600.147; 600.120 |
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no |
| Call Number |
Admin @ si @ FBW2021 |
Serial |
3559 |
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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 |
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Pages |
62-73 |
| Keywords |
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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; OR; 600.109; 600.141; 600.120 |
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no |
| Call Number |
Admin @ si @ FGW2019 |
Serial |
3264 |
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| Author |
Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
| Title |
Saliency Estimation Using a Non-Parametric Low-Level Vision Model |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
433-440 |
| Keywords |
Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms |
| Abstract |
Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. |
| Address |
Colorado Springs |
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1063-6919 |
ISBN |
978-1-4577-0394-2 |
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Conference |
CVPR |
| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ MVO2011 |
Serial |
1757 |
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| Author |
David Pujol Perich; Albert Clapes; Sergio Escalera |
| Title |
SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
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Issue |
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Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP. |
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HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ PCE2023 |
Serial |
4014 |
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| Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
| Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
Type |
Book Chapter |
| Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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| Volume |
8746 |
Issue |
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Pages |
135-146 |
| Keywords |
Graphics recognition; Graphics retrieval; Image classification |
| Abstract |
This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-662-44853-3 |
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| Notes |
DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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no |
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Admin @ si @ HFF2014 |
Serial |
2536 |
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| Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
| Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
Type |
Conference Article |
| Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.061; 600.056 |
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no |
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Admin @ si @ HFF2013b |
Serial |
2695 |
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| Author |
Petia Radeva; Joan Serrat |
| Title |
Rubber Snake: Implementation on Signed Distance Potential. |
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Conference Article |
| Year |
1993 |
Publication |
Vision Conference |
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187-194 |
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Zurich, Switzerland. |
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SWISS |
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ADAS;MILAB |
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no |
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ADAS @ adas @ RaS1993 |
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170 |
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Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
| Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
80 |
Issue |
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Pages |
64-82 |
| Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
| Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
Approved |
no |
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Admin @ si @ RSJ2018 |
Serial |
3096 |
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| Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas |
| Title |
Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model |
Type |
Journal Article |
| Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
13 |
Issue |
3 |
Pages |
229–241 |
| Keywords |
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| Abstract |
One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes. |
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Springer-Verlag |
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Edition |
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1433-2833 |
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|
| Notes |
DAG; IF 2009: 1,213 |
Approved |
no |
| Call Number |
DAG @ dag @ FLS2010a |
Serial |
1288 |
| Permanent link to this record |
