NMF with local constraint and Deep NMF with temporal dependencies constraint for action recognition

Tang, Ming; Chen, Yiran; Ma, Lei; Bai, He; Xing, Yue

doi:10.1007/s00521-018-3685-9

Cited by 11 publications

(9 citation statements)

References 82 publications

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“…Before, many grouping strategies on the single view data have been proposed. Ordinarily, these current single-see gathering approaches can be by and large isolated into three characterizations, specifically piece bunching approaches [22][23][24], supernatural grouping approaches [25][26][27], and subspace grouping approaches [28,29]. Part gathering approaches commonly use bit abilities to design the main commitments to a high dimensional piece space where grouping can be performed capably.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Part gathering approaches commonly use bit abilities to design the main commitments to a high dimensional piece space where grouping can be performed capably. For example, M. Tong et al [25] use a Gaussian piece to design the commitments to a divided space and wire pair savvy constraints into part sorting out some way to coordinate the pattern of the gathering. Li et al [23] use a social occasion of pre-shown bits to design the wellsprings of data and enhance the piece game plan locally to further develop bunching execution.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…As of late, deep learning has shown extraordinary execution in include portrayal undertakings [18][19][20]. Consequently, numerous analysts have brought profound learning into lattice factorization and proposed countless profound component portrayal strategies [21][22][23][24][25][26][27]. J. H. Ahn et al [21] proposed multilayer nonnegative matrix factorization (MNMF).…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Kolli

Sreed

2021

Preprint

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In data mining, clustering is the one of the efficient research concept in real time data analysis, evaluation of attribute representation in clustering is main issue in artificial intelligence related research areas. Multi labeled clustering gives high amount of valuable data, which describes the evaluation and representation of attribute be the trending concept in multi labeled categorical data analysis. Multi dimensional clustering is combined complementary data from different dimensions to provide efficient clustering results in various conditions. Different multi view clustering techniques are proposed traditionally but they can give output as single clustering with input data. Because of multiplicity, multi dimensional data can have different grouping data which are reasonable consist perspective attributes. So how to find measurable and reasonable cluster results which are represented in multi view labeled data is still challenging task, so that in this paper, we propose a novel approach i.e. Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC) to represent data as a cluster with different categories. Based on multi labeled data, first proposed approach evaluates low dimensional data using optimized matrix factorization (OMF) method and clusters the similar labeled sample data into prototype cluster of dimensional data. After that we represent data in desirable orthonormality constrained view of data using adaptive heuristic to combine complementary data from different dimensions, also provide complexity in computational analysis of data representation. Experimental results of proposed approach applied on high amount of multi view data gives scalable and efficient performance with comparison to traditional multi view related clustering approaches.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Kolli

Sreed

2021

Preprint

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“…It can handle well intra-pattern action variations, such as scale and speed variations; moreover, it is insensitive to dynamic and clutter backgrounds and even partial occlusions; it achieves an accuracy of 93.30% on the KTH dataset. Tong et al [17] presented a new nonnegative matrix factorization with local constraint and proposed a nonnegative matrix factorization with temporal dependencies constraint; the method can achieve an accuracy of 93.96% on the KTH dataset. Fu et al [18] proposed a method that uses multi-scale volumetric video representation and adaptively selects an optimal space–time scale under which the saliency of a patch is the most significant; the method can achieve an accuracy of 94.33% on the KTH dataset.…”

Section: Related Workmentioning

confidence: 99%

Human action recognition based on HOIRM feature fusion and AP clustering BOW

et al. 2019

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In this paper, we propose a human action recognition method using HOIRM (histogram of oriented interest region motion) feature fusion and a BOW (bag of words) model based on AP (affinity propagation) clustering. First, a HOIRM feature extraction method based on spatiotemporal interest points ROI is proposed. HOIRM can be regarded as a middle-level feature between local and global features. Then, HOIRM is fused with 3D HOG and 3D HOF local features using a cumulative histogram. The method further improves the robustness of local features to camera view angle and distance variations in complex scenes, which in turn improves the correct rate of action recognition. Finally, a BOW model based on AP clustering is proposed and applied to action classification. It obtains the appropriate visual dictionary capacity and achieves better clustering effect for the joint description of a variety of features. The experimental results demonstrate that by using the fused features with the proposed BOW model, the average recognition rate is 95.75% in the KTH database, and 88.25% in the UCF database, which are both higher than those by using only 3D HOG+3D HOF or HOIRM features. Moreover, the average recognition rate achieved by the proposed method in the two databases is higher than that obtained by other methods.

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“…In recent years, deep learning has exhibited outstanding performance in feature representation tasks [18][19][20]. Therefore, many researchers have introduced deep learning into matrix factorization and proposed a large number of deep feature representation methods [21][22][23][24][25][26][27]. Ahn et al [21] proposed multilayer nonnegative matrix factorization (MNMF).…”

Section: Introductionmentioning

confidence: 99%

Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Liu

Dai

et al. 2021

Wireless Communications and Mobile Computing

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Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

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NMF with local constraint and Deep NMF with temporal dependencies constraint for action recognition

Cited by 11 publications

References 82 publications

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Human action recognition based on HOIRM feature fusion and AP clustering BOW

Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

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