Rank Preserving Discriminant Analysis for Human Behavior Recognition on Wireless Sensor Networks

Tao, Dapeng; Jin, Lianwen; Wang, Yongfei; Li, Xuelong

doi:10.1109/tii.2013.2255061

Cited by 71 publications

(17 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Running and jumping were excluded from the experiments in the research of Trabelsi et al [5], Tang and Sazonov [6], Lee et al [7], and Deng et al [8]. Gupta and Dallas [9] did not report how to recognize standing and sleeping, and Tao et al [10] did not describe tests for recognizing sitting and sleeping. Alshurafa et al [11] studied only walking and running recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have used particular devices to collect the raw accelerometer data for a set of movements and various activity recognition algorithms including Artificial Neural Networks (ANN) [4,7,13], -Nearest Neighbor ( NN) [8,10,11,19], Support Vector Machines (SVM) [6,14,18], and Hidden Markov Model (HMM) [5,20]. In our study, we addressed the activity recognition algorithm using SVM for three reasons.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…Table II shows that the proposed TARerank presents the best performance among the five methods, and achieves consistent improvements over three Dep (5,10,20) settings (compared with Text baseline). The NCTC in relevance-based reranking method BR decreases because BR has the only objective of improving the relevance and neglects the diversity.…”

Section: ) Comparison Of Nctcmentioning

confidence: 77%

Image Search Reranking With Hierarchical Topic Awareness

Tian

Yang

et al. 2015

IEEE Trans. Cybern.

View full text Add to dashboard Cite

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

show abstract

“…They have exceedingly organized straight edges. The previous knowledge is extracted by model of first order conic solvers (TFOCS) [6], transform invariant (TI) and directional total variation (DTV) regularizations [7], [8]. This permits to acquire a novel curved regularizer that essential global consistency restrictions between the edges of the image.…”

Section: Literature Surveymentioning

confidence: 99%