IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
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
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
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
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Current large scale image retrieval engines rely heavily on the surrounding text information, which inevitably includes some irrelevant images in the retrieval results due to the noisy environment. To improve the retrieval performance, we propose an unsupervised web image re-ranking method by incorporating images' visual information. Our method can automatically select a set of representative images from the original image pool as concept model, which is highly related to the query concept and critically important for the re-ranking result. With a similarity graph constructed by top results given by text based retrieval, we utilize Normalized Cut to select the part with the highest similarity density as concept model. We rerank the rest images according to their similarities to the concept model. The advantages of our method are (i): Our method is unsupervised, and it doesn't need any pre-prepared query/training image or user's feedback, Thus it greatly facilitates users' retrieval. (ii): By finding a set of images rather than single image, we are able to give a more complete and more robust model for the query concept. (iii): Multiranking Integration Strategy is adopted to re-rank the rest images. Experiments show that our method can achieve satisfying results.