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Marc Bolaños; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards Storytelling from Visual Lifelogging: An Overview |
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2017 |
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IEEE Transactions on Human-Machine Systems |
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THMS |
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47 |
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1 |
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77 - 90 |
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Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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MILAB; 601.235 |
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Admin @ si @ BDR2017 |
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2712 |
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Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva |
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Title |
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation |
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Journal Article |
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2017 |
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Computer Vision and Image Understanding |
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CVIU |
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155 |
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55-69 |
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While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. |
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MILAB; 601.235 |
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Admin @ si @ DBT2017 |
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2714 |
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Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
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Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
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2016 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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28 |
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8 |
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1548-1568 |
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Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
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Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
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HuPBA;MILAB; |
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Admin @ si @ COC2016 |
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2718 |
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Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
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Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
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2016 |
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International Journal of Computer Vision |
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IJCV |
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118 |
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1 |
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49–64 |
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Contextual rescoring; Poselets; Human pose estimation |
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In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
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Springer US |
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0920-5691 |
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HuPBA;MILAB; |
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no |
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Admin @ si @ HES2016 |
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2719 |
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Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
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Journal Article |
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Year |
2016 |
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IEEE Transaction on Geoscience and Remote Sensing |
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TGRS |
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54 |
Issue |
3 |
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1349 - 1362 |
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This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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0196-2892 |
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LAMP; 600.079;MILAB |
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Admin @ si @ RGC2016 |
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2723 |
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