title: Rank preserving sparse learning for Kinect based scene classification.
abstract: 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.
authors.forename: Dapeng
authors.initials: D
authors.lastname: Tao
authors.forename: Lianwen
authors.initials: L
authors.lastname: Jin
authors.forename: Zhao
authors.initials: Z
authors.lastname: Yang
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016287:Computer Peripherals; D003198:Computer Simulation; D003199:Computer Systems; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers; D018910:Video Games; D051598:Whole Body Imaging / Q000295:instrumentation / Q000379:methods*