TY - JOUR
T1 - Supervised manifold learning for media interestingness prediction
AU - LIU, Yang
AU - Gu, Zhonglei
AU - CHEUNG, Yiu Ming
N1 - Funding Information:
The authors would like to thank the reviewer for the helpful comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61503317.
PY - 2016/11/22
Y1 - 2016/11/22
N2 - In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwhile, we introduce a new dimensionality reduction method dubbed Supervised Manifold Regression (SMR) to learn the compact representations for predicting the continuous interestingness levels. Finally, we use the nearest neighbor classifier and support vector regressor for classification and regression, respectively. Experimental results demonstrate the effectiveness of the low-dimensional features learned by NMMP and SMR.
AB - In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwhile, we introduce a new dimensionality reduction method dubbed Supervised Manifold Regression (SMR) to learn the compact representations for predicting the continuous interestingness levels. Finally, we use the nearest neighbor classifier and support vector regressor for classification and regression, respectively. Experimental results demonstrate the effectiveness of the low-dimensional features learned by NMMP and SMR.
UR - http://www.scopus.com/inward/record.url?scp=85006320562&partnerID=8YFLogxK
UR - http://ceur-ws.org/Vol-1739/
UR - http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_29.pdf
M3 - Conference article
AN - SCOPUS:85006320562
VL - 1739
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 2016 Multimedia Benchmark Workshop, MediaEval 2016
Y2 - 20 October 2016 through 21 October 2016
ER -