Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object Mining Using a Matching Graph on Very Large Image Collections
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
| Hi-index | 0.00 |
In this paper, we propose a new image search method, called "panoramic image search", and show its application to similar landscape discovery. In order to perform the "panoramic image search", we introduce an image ranking method called PanoramaRank: a combination of image similarity and image adjacency, where image similarity is the retrieval score obtained from the classic vocabulary tree based image retrieval framework, and image adjacency is computed using a RANSAC verified SURF matching process. Our proposing notion means to search for images physically surrounded to given query image(s). A landscape is a view of an area comprising several geographical features, having a common and meaningful atmosphere. We believe a collection of images is necessary for describing a landscape. Besides, images in this collection have to be roughly similar and roughly adjacent to each other directly or indirectly. In order to discover similar landscapes, (1)find images describing the same landscape as user-selected query image(s) by employing PanoramaRank. (2)Similar images taken in different locations are retrieved, of which belong to the same location are treated as an insufficient representation of a similar landscape to the original one. (3)PanoramaRank is applied once more to find a whole landscape for each location separately. (4)Based on several comparison criteria, landscape similarity ranking has been worked out. Moreover, images of landscapes similar to a given landscape image, especially those not presented in results based on the individual pair-wised measure, can be found. Experimental results and evaluation are also presented.