TY - GEN
T1 - Detection of changes in human affect dimensions using an Adaptive Temporal Topic model
AU - Lade, Prasanth
AU - Balasubramanian, Vineeth N.
AU - Demakethepalli Venkateswara, Hemanth
AU - Panchanathan, Sethuraman
PY - 2013/10/21
Y1 - 2013/10/21
N2 - There is an increasing demand for applications that can detect changes in human affect or behavior especially in the fields of health care and crime detection. Detection of changes in continuous human affect dimensions from multimedia data precedes the exact prediction of an emotion as a continuum. With the growth in the dimensions of emotion space there is a need to discover latent descriptors (topics) that can explain these complex states. Considering that at every time step the audio/video frames constitute a set of such latent topics, the presence and absence of changes in emotion should effect the topics in those frames. Based on this assumption an Adaptive Temporal Topic model (ATTM) based change detection algorithm is presented that, at each time step, detects whether a significant change in human affect has occurred. ATTM is a probabilistic topic model that extends Latent Dirichlet Allocation model by incorporating the temporal dependencies between human audio/video 'documents' and generates refined topics. The topics assigned to a document by ATTM are adapted to the presence or absence of a change in the affect dimension at that time step. ATTM along with different regression models has been tested on the multimodal Audio Visual Emotion Challenge (AVEC 2012) data and has shown promising results in comparison to existing temporal and non-temporal topic models.
AB - There is an increasing demand for applications that can detect changes in human affect or behavior especially in the fields of health care and crime detection. Detection of changes in continuous human affect dimensions from multimedia data precedes the exact prediction of an emotion as a continuum. With the growth in the dimensions of emotion space there is a need to discover latent descriptors (topics) that can explain these complex states. Considering that at every time step the audio/video frames constitute a set of such latent topics, the presence and absence of changes in emotion should effect the topics in those frames. Based on this assumption an Adaptive Temporal Topic model (ATTM) based change detection algorithm is presented that, at each time step, detects whether a significant change in human affect has occurred. ATTM is a probabilistic topic model that extends Latent Dirichlet Allocation model by incorporating the temporal dependencies between human audio/video 'documents' and generates refined topics. The topics assigned to a document by ATTM are adapted to the presence or absence of a change in the affect dimension at that time step. ATTM along with different regression models has been tested on the multimodal Audio Visual Emotion Challenge (AVEC 2012) data and has shown promising results in comparison to existing temporal and non-temporal topic models.
KW - Change Detection
KW - Human Emotion Recognition algorithm
KW - Topic Models
KW - Video Audio data
UR - http://www.scopus.com/inward/record.url?scp=84885571161&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885571161&partnerID=8YFLogxK
U2 - 10.1109/ICME.2013.6607627
DO - 10.1109/ICME.2013.6607627
M3 - Conference contribution
AN - SCOPUS:84885571161
SN - 9781479900152
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013
T2 - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Y2 - 15 July 2013 through 19 July 2013
ER -