TY - GEN
T1 - PLSA-based sparse representation for vehicle color classification
AU - Wang, Ssu Ying
AU - Hsieh, Jun-Wei
AU - Yan, Yilin
AU - Chen, Li Chih
AU - Chen, Duan Yu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/19
Y1 - 2015/10/19
N2 - This paper proposes a novel vehicle color classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best candidate. Because an optimization process is involved, it is very inefficient. In addition, it uses only the residual and does not consider the arrangement (or distribution) of combination coefficients of visual codes in classification. Thus, it often fails to classify categories if they are similar. In this paper, the pLSA concept is first introduced into the sparse representation to build a new classifier without using the SRC measure. The weakness of the pLSA scheme is the use of EM algorithm for updating the posteriori probability of latent class. Because it is very time-consuming, a novel weighting voting strategy is introduced to improve the pLSA scheme for recognizing objects in real time. The advantages of this classifier are: the accuracy is much higher than the SRC scheme and the efficiency is real-time in data classification. Vehicle color classification is demonstrated in this paper to prove the superiority of the new classifier.
AB - This paper proposes a novel vehicle color classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best candidate. Because an optimization process is involved, it is very inefficient. In addition, it uses only the residual and does not consider the arrangement (or distribution) of combination coefficients of visual codes in classification. Thus, it often fails to classify categories if they are similar. In this paper, the pLSA concept is first introduced into the sparse representation to build a new classifier without using the SRC measure. The weakness of the pLSA scheme is the use of EM algorithm for updating the posteriori probability of latent class. Because it is very time-consuming, a novel weighting voting strategy is introduced to improve the pLSA scheme for recognizing objects in real time. The advantages of this classifier are: the accuracy is much higher than the SRC scheme and the efficiency is real-time in data classification. Vehicle color classification is demonstrated in this paper to prove the superiority of the new classifier.
KW - Algorithm design and analysis
KW - Classification algorithms
KW - Color
KW - Dictionaries
KW - Feature extraction
KW - Image color analysis
KW - Vehicles
UR - http://www.scopus.com/inward/record.url?scp=84958616848&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2015.7301724
DO - 10.1109/AVSS.2015.7301724
M3 - Conference contribution
AN - SCOPUS:84958616848
T3 - AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance
BT - AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015
Y2 - 25 August 2015 through 28 August 2015
ER -