A temporally piece-wise fisher vector approach for depression analysis

Dhall, Abhinav; Goecke, Roland

doi:10.1109/acii.2015.7344580

Cited by 42 publications

(34 citation statements)

References 24 publications

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“…In the recent five years, there have been increasingly developing techniques of signal processing and machine learning for the detection of depression [5,7,15,21,29,31]. Stratou et al studied the nonverbal behaviour (affect, emotional variability, and motor variability) for the detection of depression and post-traumatic stress disorder [29].…”

Section: Related Workmentioning

confidence: 99%

“…In their work, a Naïve Bayes classifier was selected for making the predictions. Dhall and Goecke introduced a temporally piece-wise fisher vector approach for depression analysis [7]. In the work by Chao et al [5], long short-term memory recurrent neural networks (LSTM RNN) were used to extract sequential information from audio and video features.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Qian

Kuromiya

Ren

et al. 2019

Proceedings of the Third International Symposium on Image Computing and Digital Medicine

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In recent years, machine learning has been increasingly applied to the area of mental health diagnosis, treatment, support, research, and clinical administration. In particular, using less-invasive wearables combined with the artificial intelligence to monitor, or diagnose the mental diseases has tremendous needs in real practice. To this end, we propose a novel approach for automatic detection of major depressive disorder. Firstly, spontaneous activity physical data are recorded by a watch-type device equipped with an activity monitor. Subsequently, a bag-of-behaviour-words approach is applied to extract higher representations from the raw sensor data in an unsupervised scenario. Finally, a support vector machine is selected as the classifier to make the predictions on screening the major depressive disorder. There are 69 healthy control subjects, and 14 major depressive disorder patients involved in this study. The experimental results demonstrate the effectiveness of the proposed method in a rigorous subject-independent test, which achieves an unweighted average recall at 59.3 % (an accuracy of 66.0 %). This unweighted average recall significantly ( < .05, onetailed -test) outperforms human hand-crafted features with an unweighted average recall at 53.6 % (an accuracy of 61.7 %).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Qian

Kuromiya

Ren

et al. 2019

Proceedings of the Third International Symposium on Image Computing and Digital Medicine

View full text Add to dashboard Cite

show abstract

“…2) Fisher Vector Encoding: Fisher vector (FV) encoding [30] has been widely used in computer vision problems such as action recognition [29] and depression analysis [31], [32]. It encodes both the first and the second order statistics between the low-level (local) video/image descriptors and a Gaussian Mixture Model (GMM).…”

Section: B the Specific Recognition Modelmentioning

confidence: 99%

Generic to Specific Recognition Models for Membership Analysis in Group Videos

Mou

Tzelepis

Mezaris

et al. 2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

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Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of group membership-i.e., recognizing which group the individual in question is part of. This paper presents a novel two-phase Support Vector Machine (SVM) based specific recognition model that is learned using an optimized generic recognition model. We conduct a set of experiments using a database collected to study group analysis from multimodal cues while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed specific recognition model (52%) outperforms the generic recognition model trained across all different videos (35%) and the independent recognition model trained directly on each specific video (33%) using linear SVM.

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“…Group 1 in condition 2, watching "comedy" movie Group 4 in condition 3, watching "action" movie Group 2 in condition 1, watching "horror" movie Group 3 in condition 4, watching "adventure" movie Dhall and Goecke (2015). It encodes both the first and the second-order statistics between the low-level (local) video/image descriptors and a Gaussian Mixture Model (GMM).…”

Section: Feature Extraction 331 Low-level Feature Extractionmentioning

confidence: 99%

A deep generic to specific recognition model for group membership analysis using non-verbal cues

Mou

Tzelepis

Mezaris

et al. 2019

Image and Vision Computing

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Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of the non-verbal behaviors and how this can be utilized for analysis of group membership, i.e., recognizing which group each individual is part of. This paper presents a novel Support Vector Machine (SVM) based Deep Specific Recognition Model (DeepSRM) that is learned based on a generic recognition model. The generic recognition model refers to the model trained with data across different conditions, i.e., when people are watching movies of different types. Although the generic recognition model can provide a baseline for the recognition model trained for each specific condition, the different behaviors people exhibit in different conditions limit the recognition performance of the generic model. Therefore, the specific recognition model is proposed for each condition separately and built on top of the generic recognition model. A number of experiments are conducted using a database aiming to study group analysis while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities.

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A temporally piece-wise fisher vector approach for depression analysis

Cited by 42 publications

References 24 publications

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Generic to Specific Recognition Models for Membership Analysis in Group Videos

A deep generic to specific recognition model for group membership analysis using non-verbal cues

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