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Author |
David Aldavert; Marçal Rusiñol |
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Title |
Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting |
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Conference Article |
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Year |
2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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223 - 228 |
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Keywords |
Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information |
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Abstract |
Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 600.121 |
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no |
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Admin @ si @ AlR2018b |
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3105 |
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Author |
Mohamed Ilyes Lakhal; Hakan Cevikalp; Sergio Escalera |
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Title |
CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification |
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Conference Article |
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2018 |
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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
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137-144 |
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Vehicle Classification; Deep Learning; End-to-end Learning |
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Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset. |
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Funchal; Madeira; Portugal; January 2018 |
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VISAPP |
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HUPBA |
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no |
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Admin @ si @ LCE2018a |
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3094 |
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Author |
Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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Title |
System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture |
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2018 |
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US9946933B2 |
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US9946933B2 |
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A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. |
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ADAS; 600.118 |
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no |
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Admin @ si @ SGV2018 |
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3255 |
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Author |
Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga |
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Title |
Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research |
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2018 |
Publication |
Linguistic Approaches to Bilingualism |
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67-97 |
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two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching |
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This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods. |
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NEUROBIT; no menciona |
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no |
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Admin @ si @ SLP2018 |
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2994 |
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Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim |
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Title |
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals |
Type |
Conference Article |
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Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
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2636 - 2645 |
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Keywords |
Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling |
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Abstract |
In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ YGS2018 |
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3115 |
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Author |
David Aldavert; Marçal Rusiñol |
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Title |
Manuscript text line detection and segmentation using second-order derivatives analysis |
Type |
Conference Article |
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Year |
2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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293 - 298 |
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text line detection; text line segmentation; text region detection; second-order derivatives |
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In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 302.065; 600.121 |
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no |
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Admin @ si @ AlR2018a |
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3104 |
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Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
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Title |
Segmentation of aerial images for plausible detail synthesis |
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Journal Article |
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2018 |
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Computers & Graphics |
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CG |
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71 |
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23-34 |
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Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
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The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
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0097-8493 |
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MSIAU; 600.086; 600.118 |
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no |
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Admin @ si @ ACC2018 |
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3147 |
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Author |
Xialei Liu; Joost Van de Weijer; Andrew Bagdanov |
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Title |
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank |
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Conference Article |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition |
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7661 - 7669 |
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Task analysis; Training; Computer vision; Visualization; Estimation; Head; Context modeling |
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We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of
cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing
datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-ofthe-art results. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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LAMP; 600.109; 600.106; 600.120 |
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Admin @ si @ LWB2018 |
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3159 |
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Author |
Anjan Dutta; Hichem Sahbi |
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Stochastic Graphlet Embedding |
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2018 |
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IEEE Transactions on Neural Networks and Learning Systems |
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TNNLS |
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1-14 |
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Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality |
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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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DAG; 602.167; 602.168; 600.097; 600.121 |
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Admin @ si @ DuS2018 |
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3225 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva |
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Title |
Towards social pattern characterization from egocentric photo-streams |
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Journal Article |
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2018 |
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Computer Vision and Image Understanding |
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CVIU |
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171 |
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104-117 |
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Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks |
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Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams. |
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MILAB; no proj |
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Admin @ si @ ADC2018 |
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3022 |
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Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund |
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Title |
Organ Segmentation in Poultry Viscera Using RGB-D |
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2018 |
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Sensors |
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SENS |
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18 |
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1 |
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117 |
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semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN |
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We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features. |
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HUPBA; no proj |
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no |
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Admin @ si @ PVJ2018 |
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3072 |
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Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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2018 |
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Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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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 |
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Admin @ si @ RSJ2018 |
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3096 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GRK2018 |
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3102 |
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Xavier Soria; Angel Sappa; Riad I. Hammoud |
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Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
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2018 |
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Sensors |
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SENS |
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18 |
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7 |
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2059 |
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RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 |
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Admin @ si @ SSH2018 |
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3145 |
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Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen |
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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification |
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2018 |
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ISPRS Journal of Photogrammetry and Remote Sensing |
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ISPRS J |
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138 |
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74-85 |
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Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis |
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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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LAMP; 600.109; 600.106; 600.120 |
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Admin @ si @ RKW2018 |
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3158 |
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