IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Majority based ranking approach in web image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
ContextSeer: context search and recommendation at query time for shared consumer photos
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic textile image annotation by predicting emotional concepts from visual features
Image and Vision Computing
Ranking canonical views for tourist attractions
Multimedia Tools and Applications
Travelmedia: An intelligent management system for media captured in travel
Journal of Visual Communication and Image Representation
Efficient large-scale image data set exploration: visual concept network and image summarization
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Effective summarization of large-scale web images
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image collection summarization via dictionary learning for sparse representation
Pattern Recognition
Auto feature selection for object detection, can or can't?
Proceedings of the 27th Spring Conference on Computer Graphics
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
The vast majority of the features used in today's commercially deployed image search systems employ techniques that are largely indistinguishable from text-document search - the images returned in response to a query are based on the text of the web pages from which they are linked. Unfortunately, depending on the query type, the quality of this approach can be inconsistent. Several recent studies have demonstrated the effectiveness of using image features to refine search results. However, it is not clear whether (or how much) image-based approach can generalize to larger samples of web queries. Also, the previously used global features often only capture a small part of the image information, which in many cases does not correspond to the distinctive characteristics of the category. This paper explores the use of local features in the concrete task of finding the single canonical images for a collection of commonly searched-for products. Through large-scale user testing, the canonical images found by using only local image features significantly outperformed the top results from Yahoo, Microsoft and Google, highlighting the importance of having these image features as an integral part of future image search engines.