.png){kind=icon}
Return
Rank Preserving Sparse Learning for Kinect Based Scene Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2013.2264285.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rapid development of the RGB-D sensors and the promptly growing population of the low-cost Microsoft Kinect sensor, scene classification, which is a hard, yet important, problem in computer vision, has gained a resurgence of interest recently. That is because the depth of information provided by the Kinect sensor opens an effective and innovative way for scene classification. In this paper, we propose a new scheme for scene classification, which applies locality-constrained linear coding (LLC) to local SIFT features for representing the RGB-D samples and classifies scenes through the cooperation between a new rank preserving sparse learning (RPSL) based dimension reduction and a simple classification method. RPSL considers four aspects: 1) it preserves the rank order information of the within-class samples in a local patch; 2) it maximizes the margin between the between-class samples on the local patch; 3) the L1-norm penalty is introduced to obtain the parsimony property; and 4) it models the classification error minimization by utilizing the least-squares error minimization. Experiments are conducted on the NYU Depth V1 dataset and demonstrate the robustness and effectiveness of RPSL for scene classification.
Keywords:
Dimension reduction
Kinect sensor
rank preserving and sparse learning
RGB-D sensor
scene classification
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSave
ShareSaveShareSavePairwise constraints based multiview features fusion for scene classification.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
View more