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	Author	Lluis Gomez; Dimosthenis Karatzas
	Title	MSER-based Real-Time Text Detection and Tracking			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3110 - 3115
	Keywords
	Abstract	We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization. A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
	Address	Stockholm; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 601.158; 601.197; 600.077			Approved	no
	Call Number	Admin @ si @ GoK2014a			Serial	2492
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	Author	Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual
	Title	Spatial Discriminant ICA for RS-fMRI characterisation			Type	Conference Article
	Year	2014	Publication	4th International Workshop on Pattern Recognition in Neuroimaging	Abbreviated Journal
	Volume		Issue		Pages	1-4
	Keywords
	Abstract	Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.
	Address	Tübingen; June 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4799-4150-6	Medium
	Area		Expedition		Conference	PRNI
	Notes	OR;MILAB			Approved	no
	Call Number	Admin @ si @ TBI2014			Serial	2493
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	Author	Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
	Title	Fast Structural Matching for Document Image Retrieval through Spatial Databases			Type	Conference Article
	Year	2014	Publication	Document Recognition and Retrieval XXI	Abbreviated Journal
	Volume	9021	Issue		Pages
	Keywords	Document image retrieval; distance transform; MSER; spatial database
	Abstract	The structure of document images plays a signicant role in document analysis thus considerable eorts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signicant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors.
	Address	Amsterdam; September 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	SPIE-DRR
	Notes	DAG; 600.056; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ GRK2014a			Serial	2496
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	Author	Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
	Title	Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2903 - 2908
	Keywords
	Abstract	Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships. Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ GRK2014b			Serial	2497
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	Author	Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez
	Title	Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters			Type	Journal Article
	Year	2014	Publication	Expert Systems With Applications	Abbreviated Journal	EXSY
	Volume	41	Issue	16	Pages	7281–7290
	Keywords	Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks
	Abstract	Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.055; 600.057; 600.076			Approved	no
	Call Number	Admin @ si @ LPA2014			Serial	2500
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	Author	Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras
	Title	Combining Priors, Appearance and Context for Road Detection			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	15	Issue	3	Pages	1168-1178
	Keywords	Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout
	Abstract	Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076;ISE			Approved	no
	Call Number	Admin @ si @ ALG2014			Serial	2501
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	Author	Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño
	Title	Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos			Type	Conference Article
	Year	2014	Publication	CARE workshop	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
	Abstract	We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
	Address	Boston; USA; September 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CARE
	Notes	MV; DAG; 600.060; 600.047; 600.077;SIAI			Approved	no
	Call Number	Admin @ si @ NBF2014			Serial	2504
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	Author	Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta
	Title	Semantic Pyramids for Gender and Action Recognition			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	23	Issue	8	Pages	3633-3645
	Keywords
	Abstract	Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; LAMP; 601.160; 600.074; 600.079;MILAB			Approved	no
	Call Number	Admin @ si @ KWR2014			Serial	2507
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	Author	Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras
	Title	The Photometry of Intrinsic Images			Type	Conference Article
	Year	2014	Publication	27th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1494-1501
	Keywords
	Abstract	Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images.
	Address	Columbus; Ohio; USA; June 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPR
	Notes	CIC; 600.052; 600.051; 600.074			Approved	no
	Call Number	Admin @ si @ SPB2014			Serial	2506
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	Author	M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer
	Title	Adaptive color attributes for real-time visual tracking			Type	Conference Article
	Year	2014	Publication	27th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1090 - 1097
	Keywords
	Abstract	Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
	Address	Nottingham; UK; September 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPR
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ DKF2014			Serial	2509
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	Author	Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg
	Title	Painting-91: A Large Scale Database for Computational Painting Categorization			Type	Journal Article
	Year	2014	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
	Volume	25	Issue	6	Pages	1385-1397
	Keywords
	Abstract	Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0932-8092	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ KBW2014			Serial	2510
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	Author	C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger
	Title	Limitations of visual gamma corrections in LCD displays			Type	Journal Article
	Year	2014	Publication	Displays	Abbreviated Journal	Dis
	Volume	35	Issue	5	Pages	227–239
	Keywords	Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
	Abstract	A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; DAG; 600.052; 600.077; 600.074			Approved	no
	Call Number	Admin @ si @ PRK2014			Serial	2511
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	Author	C. Alejandro Parraga
	Title	Color Vision, Computational Methods for			Type	Book Chapter
	Year	2014	Publication	Encyclopedia of Computational Neuroscience	Abbreviated Journal
	Volume		Issue		Pages	1-11
	Keywords	Color computational vision; Computational neuroscience of color
	Abstract	The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag Berlin Heidelberg	Place of Publication		Editor	Dieter Jaeger; Ranu Jung
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4614-7320-6	Medium
	Area		Expedition		Conference
	Notes	CIC; 600.074			Approved	no
	Call Number	Admin @ si @ Par2014			Serial	2512
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	Author	Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
	Title	Unsupervised Deep Feature Extraction Of Hyperspectral Images			Type	Conference Article
	Year	2014	Publication	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification
	Abstract	This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features.
	Address	Lausanne; Switzerland; June 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	WHISPERS
	Notes	MILAB; LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ RGC2014			Serial	2513
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	Author	P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes
	Title	A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3074 - 3079
	Keywords	word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
	Abstract	Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ WEG2014a			Serial	2515
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