Sparse representation of local spatial-temporal features with dimensionality reduction for motion recognition

Journal article


Wang, Jin, Sun, Xiangping, Liu, Ping, She, Mary F. H. and Kong, Lingxue. (2013). Sparse representation of local spatial-temporal features with dimensionality reduction for motion recognition. Neurocomputing. 115, pp. 150 - 160. https://doi.org/10.1016/j.neucom.2013.01.012
AuthorsWang, Jin, Sun, Xiangping, Liu, Ping, She, Mary F. H. and Kong, Lingxue
Abstract

Sparse representation and compressive sensing have attracted substantial interests in computer vision. In this paper, by introducing two new classification criteria, we extended the sparse representation classification method (SRC) for individual images to classify a video that contains a group of local spatial-temporal features. A dictionary is constructed by concatenating all class-specific dictionaries, each of which is learned from a motion class. A test video is assigned to a class label based on the minimum of reconstruction errors of individual local features or overall reconstruction error. Moreover, we compared the effectiveness of the traditional Principal Component Analysis (PCA) and two compressive sensing based dimensionality reduction methods, i.e., Random Matrix projection and Hash Matrix projection in the framework of sparse representation for motion recognition. Experimental results on four public datasets including hand gesture, human facial, human action and mouse behavior demonstrate that the proposed method achieves comparable or higher recognition accuracies compared to other state-of-the-art methods in the literatures. Although the traditional PCA requires more computation to get the transformation matrix, it performs better than the Random Matrix and Hash Matrix projections using gradient features. However, when raw features (i.e., pixel values) are used, the performance of the Random Matrix and Hash Matrix projections is significantly improved.

Keywordsmotion analysis; sparse coding; compressive sensing; interest points
Year2013
JournalNeurocomputing
Journal citation115, pp. 150 - 160
PublisherElsevier B.V.
ISSN0925-2312
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neucom.2013.01.012
Scopus EID2-s2.0-84878110889
Page range150 - 160
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationNetherlands
Permalink -

https://acuresearchbank.acu.edu.au/item/86946/sparse-representation-of-local-spatial-temporal-features-with-dimensionality-reduction-for-motion-recognition

Restricted files

Publisher's version

  • 74
    total views
  • 0
    total downloads
  • 3
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Teachers' ratings of social skills and problem behaviors as concurrent predictors of students' bullying behavior
Elliott, Stephen N., Hwang, Yoon-Suk and Wang, Jin. (2019). Teachers' ratings of social skills and problem behaviors as concurrent predictors of students' bullying behavior. Journal of Applied Developmental Psychology. 60, pp. 119 - 126. https://doi.org/10.1016/j.appdev.2018.12.005
Why choose teaching? A matter of choice : Evidence from the field
Wyatt-SmithWyatt-Smith, Claire, C., Wang, Jin, Alexander, Colette, Du Plessis, Anna, Hand, Kirstine and Colbert, Peta. (2017). Why choose teaching? A matter of choice : Evidence from the field Australia: Institute for Learning Sciences and Teacher Education, Australian Catholic University.
Probabilistic latent semantic analysis for multichannel biomedical signal clustering
Wang, Jin and She, Mary. (2016). Probabilistic latent semantic analysis for multichannel biomedical signal clustering. IEEE Signal Processing Letters. 23(12), pp. 1821 - 1824. https://doi.org/10.1109/LSP.2016.2623801
An incremental algorithm for discovering routine behaviours from smart meter data
Wang, Jin, Cardell-Oliver, Rachel and Liu, Wei. (2016). An incremental algorithm for discovering routine behaviours from smart meter data. Knowledge-Based Systems. 113, pp. 61 - 74. https://doi.org/10.1016/j.knosys.2016.09.016
Patient admission prediction using a pruned fuzzy min--max neural network with rule extraction
Wang, Jin, Lim, Chee Peng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura and Freischmidt, Anton. (2014). Patient admission prediction using a pruned fuzzy min--max neural network with rule extraction. Neural Computing and Applications. 26(2), pp. 277 - 289. https://doi.org/10.1007/s00521-014-1631-z
Sparse representation with multi-manifold analysis for texture classification from few training images
Sun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue. (2014). Sparse representation with multi-manifold analysis for texture classification from few training images. Image and Vision Computing. 32(11), pp. 835 - 846. https://doi.org/10.1016/j.imavis.2014.07.001
Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis
Wang, Jin, Sun, Xiangping, Nahavandi, Saeid, Kouzani, Abbas, Wu, Yuchuan and She, Mary. (2014). Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis. Computer Methods and Programs in Biomedicine. 117(2), pp. 238 - 246. https://doi.org/10.1016/j.cmpb.2014.06.014
Human identification from ECG signals via sparse representation of local segments
Wang, Jin, She, Mary, Nahavandi, Saeid and Kouzani, Abbas. (2013). Human identification from ECG signals via sparse representation of local segments. IEEE Signal Processing Letters. 20(10), pp. 937 - 940. https://doi.org/10.1109/LSP.2013.2267593
Scale invariant texture classification via sparse representation
Sun, Xiangping, Wang, Jin, She, Mary F. H. and Kong, Lingxue. (2013). Scale invariant texture classification via sparse representation. Neurocomputing. 122, pp. 338 - 348. https://doi.org/10.1016/j.neucom.2013.06.016
Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
Wang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas. (2013). Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model. Computer Methods and Programs in Biomedicine. 111(3), pp. 629 - 641. https://doi.org/10.1016/j.cmpb.2013.05.022
Unsupervised mining of long time series based on latent topic model
Wang, Jin, Sun, Xiangping, She, Mary FH, Kouzani, Abbas and Nahavandi, Saeid. (2013). Unsupervised mining of long time series based on latent topic model. Nurocomputing. 103, pp. 93 - 103. https://doi.org/10.1016/j.neucom.2012.09.008
Supervised learning probabilistic latent semantic analysis for human motion analysis
Wang, Jin, Liu, Ping, She, Mary F. H., Kouzani, Abbas and Nahavandi, Saeid. (2013). Supervised learning probabilistic latent semantic analysis for human motion analysis. Neurocomputing. 100, pp. 134 - 143. https://doi.org/10.1016/j.neucom.2011.10.033
Bag-of-words representation for biomedical time series classification
Wang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas. (2013). Bag-of-words representation for biomedical time series classification. Biomedical Signal Processing and Control. 8(6), pp. 634 - 644. https://doi.org/10.1016/j.bspc.2013.06.004
Intelligent clothing for automated recognition of human physical activities in free-living environment
Wu, Yuchuan, Chen, Ronghua, Wang, Jin, Sun, Xiangping and She, Mary F. H.. (2012). Intelligent clothing for automated recognition of human physical activities in free-living environment. Journal of the Textile Institute. 103(8), pp. 806 - 816. https://doi.org/10.1080/00405000.2011.611641