Abstract
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.
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Zhao, M., Ohshima, H., Tanaka, K. (2012). Panoramic Image Search by Similarity and Adjacency for Similar Landscape Discovery. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_21
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DOI: https://doi.org/10.1007/978-3-642-35063-4_21
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