Congested scene classification via efficient unsupervised feature learning and density estimation

Yuan, Yuan; Wan, Jia; Wang, Qi

doi:10.1016/j.patcog.2016.03.020

Cited by 58 publications

(23 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Unsupervised classification centres around the choice of the initial class parameters and adjustments in its iteration. The task of giving land cover labels to individual spectral clusters is accomplished later, on the basis of ground data culled from the areas indicated by the resulting clusters (Yuan, Wan & Wang 2016;Cheriyadat 2014).…”

Section: Unsupervised Classificationmentioning

confidence: 99%

Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

Neware¹

2019

Preprint

View full text Add to dashboard Cite

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

show abstract

Section: Unsupervised Classificationmentioning

confidence: 99%

Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

Neware¹

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, they relied on preprocessing algorithms such as background subtraction and tracking, which limits their detection speed and accuracy. With a substantial breakthrough of deep learning technology in the field of vision tasks, some studies [9], [10], [20], [21] used the advantage of the powerful fitting ability of a convolutional neural network (CNN) to automatically mine traffic features from images. These works automatically and efficiently extracted congestion features and did not require preprocessing of images.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Traffic State Detection in Traffic Surveillance System Using a Deep Residual Squeeze-and-Excitation Network and an Improved Triplet Loss

Tang

Cai

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Although a substantial number of traffic videos have been accumulated via daily monitoring, deep learning is seldom utilized to process these data for multilevel traffic state detection. The application of deep learning is limited for two reasons: (1) the multilevel traffic state based on traffic images has not been defined. (2) The high noise information in traffic images and extremely similar features of adjacent traffic states hinder accurate detection. Based on this situation, A new definition of the image-based multilevel traffic state is proposed using the ratio of the vehicle areas to the road areas in a traffic image, and a standard image dataset, including various illuminations and vast scenes, are established. A deep residual network named TrafficNet, which is embedded with Squeeze-and-Excitation blocks and is learned by the improved triplet loss, is proposed for multilevel traffic state detection. The Squeeze-and-Excitation block effectively reduces the model's attention to noise information and focuses on road areas that are associated with traffic features in an image. The improved triplet loss maps the learned features to a metric space where the distance between features of inter-class is larger than that within the same class, which improves the discrimination of features between adjacent traffic states. Relevant experiments prove that the performance of TrafficNet, whose accuracy (Acc) in classifying 10 traffic states reaches 94.27% with the testing dataset, is much better than that of traditional deep classification models, which do not include Squeeze-and-Excitation blocks or the improved triplet loss. INDEX TERMS Multilevel traffic state, deep residual network, Squeeze-and-Excitation blocks, improved triplet loss.

show abstract

“…Miao et al [33] created a new analytical approach designated as the mixture kernel density model highly accurate estimation of wind speed probability distributions. Yuan et al [34] developed an efficient unsupervised feature learning approach with density information and the proposed method was evaluated on the assembled congested scene dataset. Rodrigues et al [35] However, there are some problems, such as over-reliance on sample banks, prior knowledge, and lack of stability.…”

Section: Introductionmentioning

confidence: 99%

Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

et al. 2018

View full text Add to dashboard Cite

show abstract

Congested scene classification via efficient unsupervised feature learning and density estimation

Cited by 58 publications

References 35 publications

Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

Multilevel Traffic State Detection in Traffic Surveillance System Using a Deep Residual Squeeze-and-Excitation Network and an Improved Triplet Loss

Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

Contact Info

Product

Resources

About