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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Pablo Pardo, Junior Fabian, Marc Oliu, et al. (2015). ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition. In IEEE International Joint Conference on Neural Networks IJCNN2015 (pp. 1–8).
Abstract: 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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Hugo Jair Escalante, Jose Martinez, Sergio Escalera, Victor Ponce, & Xavier Baro. (2015). Improving Bag of Visual Words Representations with Genetic Programming. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: 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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Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, et al. (2015). Design of the 2015 ChaLearn AutoML Challenge. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: 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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Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, & Sergio Escalera. (2015). The AutoML challenge on codalab. In IEEE International Joint Conference on Neural Networks IJCNN2015.
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Gerard Canal, Cecilio Angulo, & Sergio Escalera. (2015). Gesture based Human Multi-Robot interaction. In IEEE International Joint Conference on Neural Networks IJCNN2015.
Abstract: The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static
gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case.
A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a
natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates.
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