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Author |
Hongxing Gao |
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Title |
Focused Structural Document Image Retrieval in Digital Mailroom Applications |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this work, we develop a generic framework that is able to handle the document retrieval problem in various scenarios such as searching for full page matches or retrieving the counterparts for specific document areas, focusing on their structural similarity or letting their visual resemblance to play a dominant role. Based on the spatial indexing technique, we propose to search for matches of local key-region pairs carrying both structural and visual information from the collection while a scheme allowing to adjust the relative contribution of structural and visual similarity is presented.
Based on the fact that the structure of documents is tightly linked with the distance among their elements, we firstly introduce an efficient detector named Distance Transform based Maximally Stable Extremal Regions (DTMSER). We illustrate that this detector is able to efficiently extract the structure of a document image as a dendrogram (hierarchical tree) of multi-scale key-regions that roughly correspond to letters, words and paragraphs. We demonstrate that, without benefiting from the structure information, the key-regions extracted by the DTMSER algorithm achieve better results comparing with state-of-the-art methods while much less amount of key-regions are employed.
We subsequently propose a pair-wise Bag of Words (BoW) framework to efficiently embed the explicit structure extracted by the DTMSER algorithm. We represent each document as a list of key-region pairs that correspond to the edges in the dendrogram where inclusion relationship is encoded. By employing those structural key-region pairs as the pooling elements for generating the histogram of features, the proposed method is able to encode the explicit inclusion relations into a BoW representation. The experimental results illustrate that the pair-wise BoW, powered by the embedded structural information, achieves remarkable improvement over the conventional BoW and spatial pyramidal BoW methods.
To handle various retrieval scenarios in one framework, we propose to directly query a series of key-region pairs, carrying both structure and visual information, from the collection. We introduce the spatial indexing techniques to the document retrieval community to speed up the structural relationship computation for key-region pairs. We firstly test the proposed framework in a full page retrieval scenario where structurally similar matches are expected. In this case, the pair-wise querying method achieves notable improvement over the BoW and spatial pyramidal BoW frameworks. Furthermore, we illustrate that the proposed method is also able to handle focused retrieval situations where the queries are defined as a specific interesting partial areas of the images. We examine our method on two types of focused queries: structure-focused and exact queries. The experimental results show that, the proposed generic framework obtains nearly perfect precision on both types of focused queries while it is the first framework able to tackle structure-focused queries, setting a new state of the art in the field.
Besides, we introduce a line verification method to check the spatial consistency among the matched key-region pairs. We propose a computationally efficient version of line verification through a two step implementation. We first compute tentative localizations of the query and subsequently employ them to divide the matched key-region pairs into several groups, then line verification is performed within each group while more precise bounding boxes are computed. We demonstrate that, comparing with the standard approach (based on RANSAC), the line verification proposed generally achieves much higher recall with slight loss on precision on specific queries. |
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January 2015 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Dimosthenis Karatzas;Marçal Rusiñol |
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978-84-943427-0-7 |
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DAG; 600.077 |
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Admin @ si @ Gao2015 |
Serial |
2577 |
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Author |
Sebastian Ramos |
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Title |
Vision-based Detection of Road Hazards for Autonomous Driving |
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Report |
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Year |
2014 |
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CVC Technical Report |
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UAB; September 2014 |
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Master's thesis |
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ADAS; 600.076 |
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Admin @ si @ Ram2014 |
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2580 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
An Overview of Color Name Applications in Computer Vision |
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Conference Article |
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Year |
2015 |
Publication |
Computational Color Imaging Workshop |
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Keywords |
color features; color names; object recognition |
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In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation. |
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Saint Etienne; France; March 2015 |
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CCIW |
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LAMP; 600.079; 600.068 |
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Admin @ si @ WeK2015 |
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2586 |
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Author |
Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang |
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Title |
An Effective Solution to Double Counting Problem in Human Pose Estimation |
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Miscellaneous |
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Year |
2015 |
Publication |
Arxiv |
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Keywords |
Pose estimation; double counting problem; mix-ture of parts Model |
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Abstract |
The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.
An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015]. |
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ISE; 600.078 |
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Call Number |
Admin @ si @ GHG2015 |
Serial |
2590 |
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Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon |
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Title |
ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition |
Type |
Conference Article |
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Year |
2015 |
Publication |
IEEE International Joint Conference on Neural Networks IJCNN2015 |
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1-8 |
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Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015.
Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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Killarney; Ireland; July 2015 |
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IJCNN |
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Notes |
HuPBA; ISE; 600.063; 600.078;MV |
Approved |
no |
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Call Number |
Admin @ si @ EGB2015 |
Serial |
2591 |
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Author |
Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li |
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Title |
Enhanced Asymmetric Bilinear Model for Face Recognition |
Type |
Journal Article |
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Year |
2015 |
Publication |
International Journal of Distributed Sensor Networks |
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IJDSN |
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Pages |
Article ID 218514 |
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Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. |
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ISE; 600.063; 600.078 |
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Call Number |
Admin @ si @ GZG2015 |
Serial |
2592 |
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Author |
Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio |
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Title |
FitNets: Hints for Thin Deep Nets |
Type |
Conference Article |
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Year |
2015 |
Publication |
3rd International Conference on Learning Representations ICLR2015 |
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Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing |
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Abstract |
While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network. |
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San Diego; CA; May 2015 |
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ICLR |
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MILAB |
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no |
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Call Number |
Admin @ si @ RBK2015 |
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2593 |
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Author |
Manuel Graña; Bogdan Raducanu |
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Title |
Special Issue on Bioinspired and knowledge based techniques and applications |
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Year |
2015 |
Publication |
Neurocomputing |
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NEUCOM |
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1-3 |
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OR;MV |
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Admin @ si @ GrR2015 |
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2598 |
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Author |
Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika |
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Title |
Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics |
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Conference Article |
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2014 |
Publication |
1st Workshop on Computer Vision for Affective Computing |
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1-8 |
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Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression. |
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Singapore; November 2014 |
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ACCV |
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OR;MV |
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Admin @ si @ RBD2014 |
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2599 |
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Author |
C. Alejandro Parraga |
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Perceptual Psychophysics |
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2015 |
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Biologically-Inspired Computer Vision: Fundamentals and Applications |
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G.Cristobal; M.Keil; L.Perrinet |
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978-3-527-41264-8 |
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CIC; 600.074 |
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Admin @ si @ Par2015 |
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2600 |
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Author |
Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters |
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Title |
Fractional Programming Weighted Decoding for Error-Correcting Output Codes |
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Conference Article |
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2015 |
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Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 |
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38-50 |
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In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments. |
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Gunzburg; Germany; June 2015 |
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Springer International Publishing |
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978-3-319-20247-1 |
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MCS |
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HuPBA;MILAB |
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Admin @ si @ ISS2015 |
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2601 |
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Author |
Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro |
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Title |
Improving Bag of Visual Words Representations with Genetic Programming |
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Conference Article |
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2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image
categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm. |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MV |
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no |
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Admin @ si @ EME2015 |
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2603 |
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Author |
Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Alexander Statnikov; Evelyne Viegas |
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Title |
Design of the 2015 ChaLearn AutoML Challenge |
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Conference Article |
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Year |
2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code
submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results.
There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is “blind tested” on
datasets the participants have never seen before, and Tweakathon phases giving time (' 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real world
applications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MILAB |
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Admin @ si @ GBC2015a |
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2604 |
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Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell |
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Quasi-real time digital assessment of Central Airway Obstruction |
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2015 |
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3rd European congress for bronchology and interventional pulmonology ECBIP2015 |
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Barcelona; Spain; April 2015 |
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ECBIP |
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IAM; MV; 600.075 |
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SGT2015 |
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2612 |
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Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
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Quality Assessment of Optical Flow in Tagging MRI |
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2015 |
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5th Dutch Bio-Medical Engineering Conference BME2015 |
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The Netherlands; January 2015 |
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BME |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ KMF2015 |
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2616 |
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