Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we introduce locality-constrained linear coding (LLC) for Kinect image classification. First we extract visual features from the RGB-D (RGB + depth) images. Then we conduct LLC feature representations on RGB and depth visual features separately, and the two representations are concatenated for achieving better discriminative capability. The PCA dimensionality reduction method is used for eliminating data redundancy between RGB image and depth image. Finally support vector machine (SVM) is applied for classification. Experiments on scene and object classification using two recent publicly available datasets ([9], [15]) demonstrate that our proposed approach has achieved superior performance comparing with state-of-the-art methods.