[1] FONG B,
WESTERINK J. Affective computing in consumer
electronics.
IEEE Transactions on Affective Computing, 2012,
3(2): 129
-131.
[2] MELINTE D
O, VLADAREANU L. Facial expressions recognition
for human-robot
interaction using deep convolutional neural
networks with
rectified adam optimizer. Sensors, 2020, 20(8):
Article 2393.
[3] ROSA R L,
RODRIGUEZ D Z, BRESSAN G. Music
recommendation
system based on user's sentiments extracted from
social
networks. IEEE Transactions on Consumer Electronics,
2015, 61(3):
359 -367.
[4] ALAMEDA-PINEDA
X, RICCI E, YAN Y, et al. Recognizing
emotions from
abstract paintings using non-linear matrix
completion.
Proceedings of the 2016 IEEE Conference on
Computer
Vision and Pattern Recognition (CVPR'16), 2016, Jun
27 - 30, Las
Vegas, NV, USA. Piscataway, NJ, USA: IEEE,
2016: 5240
-5248.
[5] MACHAJDIK
J, HANBURY A. Affective image classification
using features
inspired by psychology and art theory. Proceedings
of the 18th
ACM international conference on Multimedia
(MM'10), 2010,
Oct 25 -29, Florence, Italy. New York, NY,
USA: ACM,
2010: 83 -92.
[6] SHINDE S
R, SABALE S, KULKARNI S, et al. Experiments on
content based
image classification using color feature extraction.
Proceedings of
the 2015 International Conference on
Communication,
Information & Computing Technology
(ICCICT'15),
2015, Jan 15 - 17, Mumbai, India. Piscataway,
NJ, USA: IEEE,
2015: 1 -6.
[7] YANULEVSKAYA
V, VAN GEMERT J C, ROTH K, et al.
Emotional
valence categorization using holistic image features.
Proceedings of
the 15th IEEE International Conference on Image
Processing,
2008, Oct 12 - 15, San Diego, CA, USA.
Piscataway,
NJ, USA: IEEE, 2008: 101 -104.
[8] ZHAO S C,
GAO Y, JIANG X L, et al. Exploring principles-of-
art features
for image emotion recognition. Proceedings of the 22nd
ACM
International Conference on Multimedia (MM'14), 2014,
Nov 3 - 7,
Orlando, FL, USA. New York, NY, USA: ACM,
2014: 47 -56.
[9] BORTH D,
JI R R, CHEN T, et al. Large-scale visual sentiment
ontology and
detectors using adjective noun pairs. Proceedings of
the 21st ACM
International Conference on Multimedia (MM'13),
2013, Oct 21 -
25, Barcelona, Spain. New York, NY, USA:
ACM, 2013: 223
-232.
[10] ZHANG J,
CHEN M, SUN H, et al. Object semantics sentiment
correlation
analysis enhanced image sentiment classification.
Knowledge-Based
Systems, 2020, 191(C): Article 105245.
[11] NAG P,
SANGSKRITI S, JANNAT M E. A closer look into
Paintings'style
using convolutional neural network with transfer
learning.
Proceedings of the 11th International Joint Conference on
Computational
Intelligence ( IJCCI'19), 2019, Sept 17 - 19,
Vienna,
Austria. Lisbon, Portugal: SciTePress ( Science and
Technology
Publications, LDA), 2020: 317 -328.
[12] KARNADE A
M, DALVI P, KALBANDE D R. Emotion
identification
using CNN-based transfer learning. Advanced
Computing
Technologies and Applications: Proceedings of the 2nd
International
Conference on Advanced Computing Technologies and
Applications
(ICACTA´20), 2020, Feb 28 -29,
Mumbai, India.
Singapore:
Springer, 2020: 337 -344.
[13] JAQUETTI
P F, PILLA V J, BORBA G B, et al. VGG FACE
Fine-tuning
for classification of facial expression images of emotion.
Proceedings of
the XXVII Brazilian Congress in Biomedical
Engineering
(CBEB'20), 2020, Oct 26 - 30, VitÓria, Brazil.
2022: 1539
-1546.
[14] ZHANG B,
QUAN C Q, REN F J. Study on CNN in the
recognition of
emotion in audio and images. Proceedings of the
IEEE/ ACIS
15th International Conference on Computer and
Information
Science (ICIS'16), 2016, Jun 26 - 29, Okayama,
Japan.
Piscataway, NJ, USA: IEEE, 2016: 1 -5.
[15] RAO T R,
XU M, XU D. Learning multi-level deep
representations
for image emotion classification. arXiv Preprint,
arXiv: 1611.
07145, 2016.
[16] ZHAO S C,
LIN C, XU P F, et al. CycleEmotionGAN: Emotional
semantic
consistency preserved cyclegan for adapting image
emotions.
Proceedings of the 33rd AAAI Conference on Artificial
Intelligence
(AAAI'19), 2019, Jan 27 - Feb 1, Honolulu, HI,
USA. Menlo
Park, CA, USA: AAAI, 2019: 2620 -2627.
[17] ZHANG W,
HE X Y, LU W Z. Exploring discriminative
representations
for image emotion recognition with CNNs. IEEE
Transactions
on Multimedia, 2020, 22(2): 515 -523.
[18] YANG J F,
SHE D Y, LAI Y K, et al. Retrieving and classifying
affective
images via deep metric learning. Proceedings of the 32nd
AAAI
Conference on Artificial Intelligence ( AAAI'18), 2018,
Feb 2 - 7, New
Orleans, LA, USA. Menlo Park, CA, USA:
AAAI, 2018:
491 -498.
[19] ZHU X G,
LI L, ZHANG W G, et al. Dependency exploitation: a
unified CNN-RNN
approach for visual emotion recognition.
Proceedings of
the 26th International Joint Conference on Artificial
Intelligence (
IJCAI'17 ), 2017, Aug 19 - 25, Melbourne,
Australia. San
Francisco, CA, USA: Morgan Kaufmann, 2017:
3595 -3601.
[20]
BARRIONUEVO C, IERACHE J, SATTOLO I. Emotion
recognition
through facial expressions using supervised learning
with logistic
regression. Computer Science: Proceedings of the
26th Argentine
Congress ( CACIC'20), 2020, Oct 5 - 9, San
Justo, Buenos
Aires, Argentina. CCIS 1409. 2021: 233 -246.
[21] KUMAR C
M, SHIVAKUMAR G. A thermal imaging based
classification
of affective states using multiclass SVM. Intelligent
Systems Design
and Applications: Proceedings of the 18th
International
Conference on Intelligent Systems Design and
Applications
(ISDA'18), 2018, Dec 6 -8, Vellore, India. AISC
940. 2020: 53
-63.
[22] PATIL S,
PATIL Y M. Face expression recognition using SVM and
KNN classifier
with HOG features. Applied Computational
Technologies:
Proceedings of the 2022 International Conference on
Computing in
Engineering & Technology (ICCET'22), 2022, Feb
12 - 13,
Lonere, India. SIST 303. Singapore: Springer, 2022:
416 -424.
[23] TIWARI K,
PATEL M. Facial expression recognition using
random forest
classifier. Proceedings of the International
Conference on
Artificial Intelligence: Advances and Applications
2019 ( ICAIAA'19
), 2019, Jun 28 - 29, Jaipur, India.
Singapore:
Springer, 2020: 121 -130.
[24] HUANG R
L, DONG H B, YIN G S, et al. Ensembling 3D CNN
framework for
video recognition. Proceedings of the 2019
International
Joint Conference on Neural Networks (IJCNN'19),
2019, Jul 14 -
19, Budapest, Hungary. Piscataway, NJ, USA:
IEEE, 2019: 1
-7.
[25] VINAY A,
SAMPAT P R, BELAVADI S V, et al. Face
recognition
using interest points and ensemble of classifiers.
Proceedings of
the 4th International Conference on Recent
Advances in
Information Technology (RAIT'18), 2018, Mar 15 -
17, Dhanbad,
India. Piscataway, NJ, USA: IEEE, 2018: 1 -8.
[26] OLUWAFEMI
A G, WANG Z H. Multi-Class weather
classification
from still image using said ensemble method.
Proceedings of
the 2019 Southern African Universities Power
Engineering
Conference/ Robotics and Mechatronics/ Pattern
Recognition
Association of South Africa ( SAUPEC/ RobMech/
PRASA'19),
2019, Jan 28 - 30, Bloemfontein, South Africa.
Piscataway,
NJ, USA: IEEE, 2019: 135 -140.
[27] YANG H,
ZHANG Y S, DING W Z. Multiple heterogeneous P-
DCNNs ensemble
with stacking algorithm: A novel recognition
method of
space target ISAR images under the condition of small
sample set.
IEEE Access, 2020, 8: 75543 -75570.
[28] SU Z B,
LIU B, ZHOU X Y, et al. Multidimensional sentiment
recognition of
film and television scene images. Journal of
Electronic
Imaging. 2021, 30(6): Article 063014.
[29] SU Z B,
QIAN Y H, GU Y, et al. Research on emotion space of
film and
television scene images based on subjective perception.
The Journal of
China Universities of Posts and
Telecommunications,
2019, 26(1): 75 -81.
[30] MACHAJDIK
J, HANBURY A. Affective image classification
using features
inspired by psychology and art theory. Proceedings
of the 18th
ACM International Conference on Multimedia
(MM'10), 2010,
Oct 25 -29, Florence, Italy. New York, NY,
USA: ACM,
2010: 83 -92.
[31] ACHANTA
R, HEMAMI S, ESTRADA F, et al. Frequency tuned
salient region
detection. Proceedings of the 2009 IEEE Conference
on Computer
Vision and Pattern Recognition (CVPR'09), 2009,
Jun 20 - 25,
Miami, FL, USA. Piscataway, NJ, USA: IEEE,
2009: 1597
-1604.
[32] LIU Z,
MAO H Z, WU C Y, et al. A convnet for the 2020s.
Proceedings of
the 2022 IEEE/ CVF Conference on Computer
Vision and
Pattern Recognition (CVPR'22), 2022, Jun 18 -24,
New Orleans,
LA, USA. Piscataway, NJ, USA: IEEE, 2022:
11976 -11986.
[33] TAN M X,
LE Q V. Efficientnet: Rethinking model scaling for
convolutional
neural networks. Proceedings of the 36th
International
Conference on Machine Learning (ICML'19), 2019,
Jun 9 -15,
Long Beach, CA, USA. PMLR 97. Red Hook, NY,
USA: Curran
Associates, 2019: 6105 -6114.
[34] FEI X H,
ZHANG Q, LING Q. Vehicle exhaust concentration
estimation
based on an improved stacking model. IEEE Access,
2019, 7:
179454 -179463.
[35] YADAV A,
VISHWAKARMA D K. A deep learning architecture
of RA-DLNet
for visual sentiment analysis. Multimedia Systems,
2020, 26: 431
-451.
[36]
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image
is worth 16x16
words: Transformers for image recognition at scale.
Proceedings of
the 9th International Conference on Learning
Representations
(ICLR'21), 2021, May 3 -7, Vienna, Austria.
2021: 1 -21.
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