@article{Zhang2013,
abstract = {Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database.},
author = {<NAME> <NAME> and <NAME>},
doi = {10.1109/TIP.2012.2214045},
file = {:E$\backslash$:/Mendeley Papers/2013/IEEE Transactions on Image Processing/2013 - IEEE Transactions on Image Processing - Local energy pattern for texture classification using self-adaptive quantization threshol.pdf:pdf},
isbn = {1057-7149},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Dynamic texture,local energy pattern (LEP),self-adaptive quantization thresholds,steerable filter,texture representation},
number = {2},
pages = {31--42},
pmid = {22910113},
title = {{Local energy pattern for texture classification using self-adaptive quantization thresholds}},
volume = {22},
year = {2013}
}
@article{Chen2009,
abstract = {Information fusion offers a promising solution to the development of a high-performance classification system. In this paper, the problem of multiple gait features fusion is explored with the framework of the factorial hidden Markov model (FHMM). The FHMM has a multiple-layer structure and provides an alternative to combine several gait features without concatenating them into a single augmented feature. Besides, the feature concatenation is used to directly concatenate the features and the parallel HMM (PHMM) is introduced as a decision-level fusion scheme, which employs traditional fusion rules to combine the recognition results at decision level. To evaluate the recognition performances, McNemar's test is employed to compare the FHMM feature-level fusion scheme with the feature concatenation and the PHMM decision-level fusion scheme. Statistical numerical experiments are carried out on the Carnegie Mellon University motion of body and the Institute of Automation of the Chinese Academy of Sciences gait databases. The experimental results demonstrate that the FHMM feature-level fusion scheme and the PHMM decision-level fusion scheme outperform feature concatenation. The FHMM feature-level fusion scheme tends to perform better than the PHMM decision-level fusion scheme when only a few gait cycles are available for recognition.},
author = {<NAME> <NAME> <NAME> <NAME> <NAME>},
doi = {10.1109/TSMCC.2008.2001716},
file = {:E$\backslash$:/Mendeley Papers/2009/IEEE Transactions on Systems, Man and Cybernetics Part C Applications and Reviews/2009 - IEEE Transactions on Systems, Man and Cybernetics Part C Applications and Reviews - Factorial HMM and parallel HMM for gait recog.pdf:pdf},
issn = {10946977},
journal = {IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews},
keywords = {Factorial hidden Markov model (FHMM),Gait recognition,Information fusion,McNemar's test,Parallel HMM (PHMM),Performance evaluation},
number = {1},
pages = {114--123},
title = {{Factorial HMM and parallel HMM for gait recognition}},
volume = {39},
year = {2009}
}