Browsing by Subject "Graph-based"
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Item Open Access Alignment of uncalibrated images for multi-view classification(IEEE, 2011) Arık, Sercan Ömer; Vuraf, E.; Frossard P.Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of images is necessary prior to distance computation. We propose a method for the registration of uncalibrated images that capture the same 3D scene or object. We model the depth map of the scene as an algebraic surface, which yields a warp model in the form of a rational function between image pairs. The warp model is computed by minimizing the registration error, where the registered image is a weighted combination of two images generated with two different warp functions estimated from feature matches and image intensity functions in order to provide robust registration. We demonstrate the flexibility of our alignment method by experimentation on several wide-baseline image pairs with arbitrary scene geometries and texture levels. Moreover, the results on multi-view image classification suggest that the proposed alignment method can be effectively used in graph-based classification algorithms for the computation of pairwise distances where it achieves significant improvements over distance computation without prior alignment. © 2011 IEEE.Item Open Access Finding compound structures in images using image segmentation and graph-based knowledge discovery(IEEE, 2009-07) Zamalieva, Daniya; Aksoy, Selim; Tilton J. C.We present an unsupervised method for discovering compound image structures that are comprised of simpler primitive objects. An initial segmentation step produces image regions with homogeneous spectral content. Then, the segmentation is translated into a relational graph structure whose nodes correspond to the regions and the edges represent the relationships between these regions. We assume that the region objects that appear together frequently can be considered as strongly related. This relation is modeled using the transition frequencies between neighboring regions, and the significant relations are found as the modes of a probability distribution estimated using the features of these transitions. Experiments using an Ikonos image show that subgraphs found within the graph representing the whole image correspond to parts of different high-level compound structures. ©2009 IEEE.Item Open Access GCap: Graph-based automatic image captioning(IEEE, 2004) Pan J.-Y.; Yang H.-J.; Faloutsos C.; Duygulu, PınarGiven an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement). © 2004 IEEE.Item Open Access A graph based approach to estimating lexical cohesion(ACM, 2008) Gürkök, Hayrettin; Karamuftuoglu, Murat; Schaal, MarkusTraditionally, information retrieval systems rank documents according to the query terms they contain. However, even if a document may contain all query terms, this does not guarantee that it is relevant to the query. The query terms can occur together in the same document, but may have been used in different contexts, expressing separate topics. Lexical cohesion is a characteristic of natural language texts, which can be used to determine whether the query terms are used in the same context in the document. In this paper we make use of a graph-based approach to capture term contexts and estimate the level of lexical cohesion in a document. To evaluate the performance of our system, we compare it against two benchmark systems using three TREC document collections. Copyright 2008 ACM.