Name
Abstract | ||
|---|---|---|
This paper proposes a novel object 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. Two applications are demonstrated in this paper to prove the superiority of the new classifier, i.e., vehicle make and model recognition, and action analysis. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.232 | ICPR |
Keywords | Field | DocType |
optimisation,object classification method,expectation-maximisation algorithm,image representation,posteriori probability,optimization process,object classification,data classification,vision-based applications,objects recognition,plsa,image reconstruction,sparse representation, plsa, object classification,visual codes,image classification,probabilistic latent semantic analysis,object recognition,sparse reconstruction cost,plsa-based sparse representation,src measure,weighting voting strategy,sparse representation,em algorithm,probability,latent class | Residual,Weighting,Pattern recognition,Computer science,Expectation–maximization algorithm,Sparse approximation,Artificial intelligence,Probabilistic latent semantic analysis,Data classification,Classifier (linguistics),Machine learning | Conference |
ISSN | Citations | PageRank |
1051-4651 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilin Yan | 1 | 145 | 10.94 |
| Jun-Wei Hsieh | 2 | 751 | 67.88 |
| Hui-Fen Chiang | 3 | 7 | 2.49 |
| Shyi-chyi Cheng | 4 | 20 | 6.47 |
| Duan-Yu Chen | 5 | 296 | 28.79 |