Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature context for image classification and object detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image classification by non-negative sparse coding, low-rank and sparse decomposition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Salient coding for image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Beyond bag of words: image representation in sub-semantic space
Proceedings of the 21st ACM international conference on Multimedia
Undo the codebook bias by linear transformation for visual applications
Proceedings of the 21st ACM international conference on Multimedia
Laplacian affine sparse coding with tilt and orientation consistency for image classification
Journal of Visual Communication and Image Representation
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Recently, the bag-of-visual-words (BoW) model has been proven very effective for image classification. However, most researchers used local features directly while neglecting their spatial information and correlations. Besides, the encoding of local features causes some information loss which also hinders the final image classification performance. To tackle these problems, in this paper, we proposed a novel image classification method using Harr-like transformation of local features with additional consideration of coding residuals. We apply Harr-like transformation on local features to combine the spatial information as well as the correlations of local features. These Harr-like transformed local features are then encoded using non-negative sparse coding. We jointly consider the coding parameters and the coding residuals as the local representation in order to reduce the information loss during the local feature encoding process. Experiments on several public datasets demonstrate the effectiveness of the proposed method.