TY - JOUR
T1 - Efficient classification with sparsity augmented collaborative representation
AU - Akhtar, Naveed
AU - Shafait, Faisal
AU - Mian, Ajmal
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It is a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that makes the scheme effective. This claim is attractive as it allows to relinquish the computationally expensive sparsity constraint over the representation. In this paper, we first extend the analysis supporting this claim and then show that sparseness explicitly contributes to improved classification, hence it should not be completely ignored for computational gains. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method that capitalizes on the resulting representation. The augmented representation and the classification method work together meticulously to achieve higher accuracy and lower computational time compared to state-of-the-art collaborative representation based classification approaches. Experiments on benchmark face, object, action and scene databases show the efficacy of our approach.
AB - Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It is a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that makes the scheme effective. This claim is attractive as it allows to relinquish the computationally expensive sparsity constraint over the representation. In this paper, we first extend the analysis supporting this claim and then show that sparseness explicitly contributes to improved classification, hence it should not be completely ignored for computational gains. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method that capitalizes on the resulting representation. The augmented representation and the classification method work together meticulously to achieve higher accuracy and lower computational time compared to state-of-the-art collaborative representation based classification approaches. Experiments on benchmark face, object, action and scene databases show the efficacy of our approach.
KW - Collaborative representation
KW - Multi-class classification
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85010635550&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2016.12.017
DO - 10.1016/j.patcog.2016.12.017
M3 - Article
AN - SCOPUS:85010635550
SN - 0031-3203
VL - 65
SP - 136
EP - 145
JO - Pattern Recognition
JF - Pattern Recognition
ER -