Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Generic image classification using visual knowledge on the web
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A bootstrapping framework for annotating and retrieving WWW images
Proceedings of the 12th annual ACM international conference on Multimedia
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
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
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Visual pattern discovery using web images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image collector III: a web image-gathering system with bag-of-keypoints
Proceedings of the 16th international conference on World Wide Web
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel region-based approach to visual concept modeling using web images
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Hi-index | 0.00 |
We propose a new Web image selection method which employs the region-based bag-of-features representation. The contribution of this work is (1) to introduce the region-based bag-of-features representation into an Web image selection task where training data is incomplete, and (2) to prove its effectiveness by experiments with both generative and discriminative machine learning methods. In the experiments, we used a multiple-instance learning SVM and a standard SVM as discriminative methods, and pLSA and LDA mixture models as probabilistic generative methods. Several works on Web image filtering task with bag-of-features have been proposed so far. However, in case that the training data includes much noise, sufficient results could not be obtained. In this paper, we divide images into regions and classify each region instead of classifying whole images. By this region-based classification, we can separate foreground regions from background regions and achieve more effective image training from incomplete training data. By the experiments, we show that the results by the proposed methods outperformed the results by the whole-image-based bag-of-features.