Automatic Classification of Single Facial Images
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
Topographic Independent Component Analysis
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple similarities based kernel subspace learning for image classification
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Sparse coding on local spatial-temporal volumes for human action recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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In this paper, we propose a topographic subspace learning algorithm, named key-coding learning, which utilizes irrelevant unlabeled auxiliary data to facilitate image classification and retrieval tasks. It is worth noticing that we do not need to assume the auxiliary data follows the same class labels or generative distribution as the target training data. Firstly, the subspace model is learnt from enormous scale- and rotation-invariant SURF descriptors extracted from auxiliary and training images, which makes model insensitive to geometric and photometric image transformation. Then the bases of model are pooled by clustering to generate topographic basis banks. We provide insights to show that the topographic model is highly biologically plausible in simulating the complex cells in the visual cortex. Finally we generate the succinct sparse representations by mapping target data into this topographic model. Due to the capability of transferring knowledge, the proposed topographic subspace model can effectively address insufficient training data problem for image classification and is also helpful for generating discriminative features for image retrieval. Intensive experiments are conducted on three image datasets to evaluate the performance of our proposed model, the experimental results are encouraging and promising.