Human action recognition with graph-based multiple-instance learning

Yi, Yang; Lin, Maoqing

doi:10.1016/j.patcog.2015.11.022

Cited by 48 publications

(17 citation statements)

References 56 publications

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“…Among most intelligent algorithms, swarm intelligence can be considered one type of artificial intelligence concept or technique that was inspired by the natural phenomenon of a flock of birds searching for food sources by changing their locations based on their former position and swarm position. Particle swarm optimization (PSO) is one of these swarm techniques and was first introduced by Kennedy J, Eberhart R in 1995 [1][59]. Particle swarm optimization is similar to other population-based meta-heuristic optimization techniques in that it first initializes a group of individuals as a population and then updates the information (state) of these individuals by an evolution process.…”

Section: Methodsmentioning

confidence: 99%

“…R 1 and R 2 are randomly generated values in the domain of [0, 1]. W denotes an inertia weight that was first proposed by Shi and Eberhart [1][57]. C 1 and C 2 are positive acceleration coefficients, which are also called cognitive and social parameters due to their role in the algorithm evolution procedure.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, deep learning techniques have been widely developed and applied in many real-world applications due to their excellent performance on large-scale and high-dimensional datasets. Deep learning techniques have been successfully applied in domains such as human action recognition [1–8], text processing and applications [9–14], medical image processing [15–26], and computational biology [27–36]. …”

Section: Introductionmentioning

confidence: 99%

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Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

2017

PLoS ONE

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In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that 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. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

2017

PLoS ONE

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“…Vishwakarma and Kapoor (2015) investigated a method hybrid classification model using SVM and k-Nearest Neighbour (k-NN) using human silhouette and grids for modelling feature vectors. A graph model-based method in Yi and Lin (2016) was proposed for multiple instance learning. Authors used a graph for presenting the local information, which expected faster than the previous subspace learning methods with computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Multiple classifier-based spatiotemporal features for living activity prediction

Hoang

2017

Journal of Information and Telecommunication

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Nowadays, the action prediction technique plays an important role in many automatic systems. There are some proposed methods for this issue. However, they retain limitations such as accuracy and computational time, especially for applying in limited resource systems. This paper presents an approach to enhance the efficiency of the activity prediction task. The work processes on multiple classifiers using spatiotemporal features based on scalable feature descriptors, such as histogram of oriented gradients (HOG), histogram of oriented optical flow (HOF), and motion boundary histogram (MBH). In order to improve prediction accuracy, two layers of classified machine models are studied for applying on spatiotemporal features with the dynamic foreground extraction process. The first layer based on unsupervised classification is proposed to construct a dictionary of features, which supports for distinguishing and uniform the number of features. The next task is that supervised machine learning supports for final decision of action classes. The proposed approach was evaluated on several benchmark datasets, which are available online. The results demonstrate that the approach enhances accuracy and efficiency of the prediction system. ARTICLE HISTORY

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“…A fundamental problem in graph-based pattern recognition is that of recovering the set of correspondences (matching) between the vertices of two graphs. In computer vision, graph matching has been applied to a wide range of problems, from object categorisation [4,1] to action recognition [14,15]. More formally, in the graph matching problem the goal is to find a mapping between the nodes of two graphs such that the edge structure is preserved.…”

Section: Introductionmentioning

confidence: 99%

The Average Mixing Matrix Signature

Rossi

Severini

Torsello

2016

Lecture Notes in Computer Science

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Abstract. Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

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Human action recognition with graph-based multiple-instance learning

Cited by 48 publications

References 56 publications

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Multiple classifier-based spatiotemporal features for living activity prediction

The Average Mixing Matrix Signature

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