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Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu |
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Saliency for fine-grained object recognition in domains with scarce training data |
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Journal Article |
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2019 |
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Pattern Recognition |
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PR |
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94 |
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62-73 |
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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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LAMP; OR; 600.109; 600.141; 600.120 |
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no |
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Admin @ si @ FGW2019 |
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3264 |
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Author |
Laura Igual; Agata Lapedriza; Ricard Borras |
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Title |
Robust Gait-Based Gender Classification using Depth Cameras |
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Journal Article |
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2013 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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37 |
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1 |
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72-80 |
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This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section. |
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MILAB; OR;MV |
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Admin @ si @ ILB2013 |
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2144 |
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Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Rendering ground truth data sets to detect shadows cast by static objects in outdoors |
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2014 |
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Multimedia Tools and Applications |
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MTAP |
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70 |
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1 |
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557-571 |
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Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection |
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In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. |
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Springer US |
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1380-7501 |
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OR;MV |
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Admin @ si @ ISR2014 |
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2229 |
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Author |
Jordi Vitria; J. Llacer |
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Title |
Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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17 |
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14 |
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1491–1498 |
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OR;MV |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra |
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Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. |
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1994 |
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Kidney International |
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46 |
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6 |
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1721-1727 |
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OR;MV |
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BCNPCL @ bcnpcl @ MSV1994 |
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113 |
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