Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Atomic Decomposition by Basis Pursuit
SIAM Review
The Journal of Machine Learning Research
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - 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
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Compression of facial images using the K-SVD algorithm
Journal of Visual Communication and Image Representation
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Hi-index | 0.00 |
We address the large scale visual classification problem. The approach is based on sparse and redundant representations over trained dictionaries. The proposed algorithm firstly trains dictionaries using the images of every visual category, one category has one dictionary. In this paper, we choose the K-SVD algorithm to train the visual category dictionary. Given a set of training images from a category, the K-SVD algorithm seeks the dictionary that leads to the best representation for each image in this set, under strict sparsity constraints. For testing images, the traditional classification method under the large scale condition is the k-nearest-neighbor method. And in our method, the category result is through the reconstruction residual using different dictionaries. To get the most effective dictionaries, we explore the large scale image database from the Internet [2] and design experiments on a nearly 1.6 million tiny images on the middle semantic level defined based on Word-Net. We compare the image classification performance under different image resolutions and k-nearest-neighbor parameters. The experimental results demonstrate that the proposed algorithm outperforms k-nearest-neighbor in two aspects: 1) the discriminative capability for large scale visual classification task, and 2) the average running time of classifying one image.