Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Image collector III: a web image-gathering system with bag-of-keypoints
Proceedings of the 16th international conference on World Wide Web
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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We propose a new Web image gathering system which employs the region-based bag-of-features representation and multiple instance learning. The contribution of this work is introducing the region-based bag-of-features representation into an Web image gathering task where training data is incomplete and having proved its effectiveness by comparing the proposed method with the normal whole-image-based bag-of-features representation. In our method, first, we perform region segmentation for an image, and next we generate a bag-of-features vector for each region. One image is represented by a set of bag-of-features vectors in this paper, while one image is represented by just one bag-of-features vector in the normal bag-of-features representation which is very popular for visual object categorization tasks recently. Several works on Web image selection 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 with multiple-instance support vector machine (mi-SVM) 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-visual-words and the normal support vector machine.