Journal ArticleDOI
Learning universal multiview dictionary for human action recognition
TLDR
A novel discriminative dictionary learning framework is proposed by formulating a universal dictionary which consists of a shared sub-dictionary and a set of class-specific sub-Dictionaries and is able to achieve better performance than a number of state-of-the-art ones.About:
This article is published in Pattern Recognition.The article was published on 2017-04-01. It has received 43 citations till now. The article focuses on the topics: K-SVD & Discriminative model.read more
Citations
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Journal ArticleDOI
Vision-based human activity recognition: a survey
TL;DR: Most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks, which establishes new trend and milestone in the development cycle of HAR systems.
Journal ArticleDOI
Multiview Learning With Robust Double-Sided Twin SVM
TL;DR: Wang et al. as mentioned in this paper developed multiview robust double-sided twin SVM (MvRDTSVM) with SVM-type problems, which introduces a set of doublesided constraints into the proposed model to promote classification performance.
Posted Content
A survey on trajectory clustering analysis
TL;DR: This paper provides a holistic understanding and deep insight into trajectory clustering, and presents a comprehensive analysis of representative methods and promising future directions.
Journal ArticleDOI
Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data.
TL;DR: The DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance.
Journal ArticleDOI
A Survey on Anomalous Behavior Detection for Elderly Care Using Dense-Sensing Networks
TL;DR: From the study, it is observed that employing sensor fusion techniques could significantly increases the efficiency of dense sensing network and sensor fusion models ensure a high level of robustness and effectiveness compared to the traditional methods.
References
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Journal ArticleDOI
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
TL;DR: It is shown that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex.
Proceedings ArticleDOI
HMDB: A large video database for human motion recognition
TL;DR: This paper uses the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube, to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions.
Proceedings ArticleDOI
Locality-constrained Linear Coding for image classification
TL;DR: This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM, using the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation.
Proceedings ArticleDOI
Linear spatial pyramid matching using sparse coding for image classification
TL;DR: An extension of the SPM method is developed, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and a linear SPM kernel based on SIFT sparse codes is proposed, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
Proceedings Article
Efficient sparse coding algorithms
TL;DR: These algorithms are applied to natural images and it is demonstrated that the inferred sparse codes exhibit end-stopping and non-classical receptive field surround suppression and, therefore, may provide a partial explanation for these two phenomena in V1 neurons.