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Image and Vision Computing | Year: 2010

Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research. © 2009 Elsevier B.V. All rights reserved.

Pattern Recognition | Year: 2012

Automatically naming faces in online social networks enables us to search for photos and build user face models. We consider two common weakly supervised settings where: (1) users are linked to photos, not to faces and (2) photos are not labeled but part of a users album. The focus is on algorithms that scale up to an entire online social network. We extensively evaluate different graph-based strategies to label faces in both settings and consider dependencies. We achieve results on a par with a recent multi-person approach, but with 60 times less computation time on a set of 300K weakly labeled faces and 1.4 M faces in user albums. A subset of the faces can be labeled with a speed-up of over three orders of magnitude. © 2011 Elsevier Ltd. All rights reserved.

2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011 | Year: 2011

Face labeling is the process of assigning names to faces. In this paper, we start from a weakly-supervised setting where names are linked to photos, not faces. We introduce two face labeling strategies that scale well to large data sets and allow for labeling parts thereof. This is a useful property especially for data sets where photos are frequently added or (re)labeled. We evaluate our and two related face labeling strategies on a novel corpus of 34,763 faces, gathered from an online social network for dance party visitors. We achieve a speed-up of an order of magnitude over the state-of-the-art approach while the labeling quality is almost unaffected. On a subset of the faces, the speed-up is even more apparent, reaching at least two orders of magnitude. © 2011 IEEE.

ICMI'12 - Proceedings of the ACM International Conference on Multimodal Interaction | Year: 2012

This position paper outlines the first stages in an ongoing PhD project on mediated social touch, and the effects mediated touch can have on someone's affective state. It is argued that touch is a profound communication channel for humans, and that communication through touch can, to some extent, occur through mediation. Furthermore, touch can be used to communicate emotions, as well as have immediate affective consequences. The design of an input device, consisting of twelve force-sensitive resistors, to study the communication of emotions through mediated touch is presented. A pilot study indicated that participants used duration of touch and force applied as ways to distinguish between different emotions. This paper will conclude by discussing possible improvements for the input device, how the pilot study fits with the overall PhD project, as well as future directions for the PhD project in general. Copyright 2012 ACM.

Bagheri A.,Isfahan University of Technology | Saraee M.,University of Salford | De Jong F.,INTERACTION MEDIA GROUP
Knowledge-Based Systems | Year: 2013

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the internet, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for these aspects. In this paper, we propose a novel unsupervised and domain-independent model for detecting explicit and implicit aspects in reviews for sentiment analysis. In the model, first a generalized method is proposed to learn multi-word aspects and then a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Second a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Third, two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. Finally the model employs an approach which uses explicit aspects and opinion words to identify implicit aspects. Utilizing extracted polarity lexicon, the approach maps each opinion word in the lexicon to the set of pre-extracted explicit aspects with a co-occurrence metric. The proposed model was evaluated on a collection of English product review datasets. The model does not require any labeled training data and it can be easily applied to other languages or other domains such as movie reviews. Experimental results show considerable improvements of our model over conventional techniques including unsupervised and supervised approaches. © 2013 Elsevier B.V. All rights reserved.

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