Hierarchical feature coding for image classification

Liu, Jingyu; Huang, Yongzhen; Wang, Liang; Wu, Shu

doi:10.1016/j.neucom.2014.04.022

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…In their work, 3D shape context has been incorporated into local spatio-temporal interest point features for human action recognition. Such hierarchical feature coding idea has also been shared by other researchers working on image classification [18].…”

Section: Introductionmentioning

confidence: 94%

Describing and learning of related parts based on latent structural model in big data

2016

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Section: Introductionmentioning

confidence: 94%

Describing and learning of related parts based on latent structural model in big data

2016

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“…The codebook-based feature encoding technique has shown excellent recognition results in numerous applications of gait recognition [9], image classification [56], and activity analysis [57]. It follows the BOVWs channel, which has two main steps: (1) codebook computation and (2) feature encoding.…”

Section: Codebook-based Feature Encodingmentioning

confidence: 99%

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Fatima,

Khan,

Nisar

et al. 2023

Sensors

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This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

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Visual feature coding based on heterogeneous structure fusion for image classification

et al. 2017

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Hierarchical feature coding for image classification

Cited by 10 publications

References 31 publications

Describing and learning of related parts based on latent structural model in big data

Describing and learning of related parts based on latent structural model in big data

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Visual feature coding based on heterogeneous structure fusion for image classification

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