TY - GEN
T1 - Novel color, shape and texture-based scene image descriptors
AU - Banerji, Sugata
AU - Sinha, Atreyee
AU - Liu, Chengjun
PY - 2012
Y1 - 2012
N2 - This paper introduces several novel color, shape and texture-based image descriptors for scene image classification with applications to image search and retrieval. Specifically, first, a new 3-Dimensional Local Binary Pattern (3D-LBP) descriptor is proposed for color image local feature extraction. Second, a new shape descriptor (HaarHOG) is introduced by combining Haar wavelet transformation and Histogram of Oriented Gradients (HOG). Third, these descriptors are fused using an optimal feature representation technique to generate a robust 3-Dimensional LBP-HaarHOG (3DLH) descriptor that can perform well on different scene image categories. Finally, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The proposed descriptors and fusion technique are evaluated using three grand challenge datasets: the MIT Scene dataset, the UIUC Sports Event dataset, and a part of the Caltech 256 dataset.
AB - This paper introduces several novel color, shape and texture-based image descriptors for scene image classification with applications to image search and retrieval. Specifically, first, a new 3-Dimensional Local Binary Pattern (3D-LBP) descriptor is proposed for color image local feature extraction. Second, a new shape descriptor (HaarHOG) is introduced by combining Haar wavelet transformation and Histogram of Oriented Gradients (HOG). Third, these descriptors are fused using an optimal feature representation technique to generate a robust 3-Dimensional LBP-HaarHOG (3DLH) descriptor that can perform well on different scene image categories. Finally, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The proposed descriptors and fusion technique are evaluated using three grand challenge datasets: the MIT Scene dataset, the UIUC Sports Event dataset, and a part of the Caltech 256 dataset.
UR - http://www.scopus.com/inward/record.url?scp=84871596449&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871596449&partnerID=8YFLogxK
U2 - 10.1109/ICCP.2012.6356193
DO - 10.1109/ICCP.2012.6356193
M3 - Conference contribution
AN - SCOPUS:84871596449
SN - 9781467329514
T3 - Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
SP - 245
EP - 248
BT - Proceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
T2 - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Y2 - 30 August 2012 through 1 September 2012
ER -