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Handwriting Recognition Accuracy Improvement by Author Identification
[chapter]
2006
Lecture Notes in Computer Science
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Proposed document identification procedure recognizes frame lines of the image, matches them to lines on the template, calculates the matching factor. and uses it as dissimilarity measure d(f k , o). ...
based on relation between fixed graphical elements extracted from the image and fixed elements of the document template. ...
doi:10.1007/11785231_71
fatcat:skfzac75prgipiixq5ao5plw5m
Model-Guided Segmentation and Layout Labelling of Document Images Using a Hierarchical Conditional Random Field
[chapter]
2009
Lecture Notes in Computer Science
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, paragraph, etc.; and 3. probabilistic layout model for encoding global relations between the above blocks for a particular class of documents. ...
The system extracts features which encode contextual information and spatial configurations of a given document image, and learns relations between these layout entities using hierarchical CRFs. ...
They model a page as a mixture of layout structures, and use a probabilistic matching algorithm for find the most probable layout. ...
doi:10.1007/978-3-642-11164-8_61
fatcat:xgaadnwbvjem5mqxz4m7eqahxq
Generative probabilistic models for multimedia retrieval: query generation against document generation
2005
IEE Proceedings - Vision Image and Signal Processing
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This paper 1 presents the use of generative probabilistic models for multimedia retrieval. ...
We estimate Gaussian mixture models to describe the visual content of images (or video) and explore different ways of using them for retrieval. ...
The end of this section fills in the details of using generative image models in the probabilistic framework of the previous section. ...
doi:10.1049/ip-vis:20045196
fatcat:jerqz4gx6vfubam6hsgnhjqih4
Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier
2009
2009 10th International Conference on Document Analysis and Recognition
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Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. ...
We present a new approach for recognition of complex graphic symbols in technical documents. ...
Introduction and related works Graphics recognition is a subfield of document image analysis and it deals with graphic entities that appear in document images. ...
doi:10.1109/icdar.2009.92
dblp:conf/icdar/LuqmanBR09
fatcat:tyk2kvhjozdtlfbt4tzzbppmge
Multi-Resolution Probabilistic Information Fusion for Camera-based Document Image Matching
2011
2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics
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image from a document database. ...
Given a part of a document image taken with any camera at an arbitrary orientation and sometimes farfrom-perfect illumination, an important problem is to match this query image to the corresponding full ...
INTRODUCTION We present a novel multi-resolution probabilistic method for matching a database document to a degraded query image (for instance, taken from a low quality camera in bad illumination and even ...
doi:10.1109/ncvpripg.2011.21
fatcat:dcjumgzd6bcwhjzvvwdfdrft3e
DARWIN: a framework for machine learning and computer vision research and development
2012
Journal of machine learning research
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The framework includes a wide range of standard machine learning and graphical models algorithms as well as reference implementations for many machine learning and computer vision applications. ...
The framework contains Matlab wrappers for core components of the library and an experimental graphical user interface for developing and visualizing machine learning data flows. ...
Probabilistic Graphical Models. ...
dblp:journals/jmlr/Gould12
fatcat:6euah46iwzgntmaqgsispvilfq
Evaluating the Rarity of Handwriting Formations
2011
2011 International Conference on Document Analysis and Recognition
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Modeling the distribution as a probabilistic graphical model several probabilities are inferred: the probability of random correspondence (PRC) as a measure of the discriminatory power of the characteristics ...
Using the most commonly occurring letter pair "th" and characteristics specified by questioned document examiners, the highest probability formation and low probability formations in a database are determined ...
Probabilistic graphical models are useful to express such independencies [11] . ...
doi:10.1109/icdar.2011.130
dblp:conf/icdar/Srihari11
fatcat:4ye6xwctgbeyffjfevpxjkhiby
On image auto-annotation with latent space models
2003
Proceedings of the eleventh ACM international conference on Multimedia - MULTIMEDIA '03
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In this paper, we apply and compare two simple latent space models commonly used in text analysis, namely Latent Semantic Analysis (LSA) and Probabilistic LSA (PLSA). ...
Furthermore, nonprobabilistic methods (LSA and direct image matching) outperformed PLSA on the same dataset. ...
The images used in this study belong to the Corel stock photo collection c . ...
doi:10.1145/957013.957070
dblp:conf/mm/MonayG03
fatcat:fqlaacw2kzeyvlu3xpdj2y3xo4
Semantic-based query formulation in PAS
1991
Open research Areas in Information Retrieval
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The system is based on the probabilistic retrieval model which involves ranking the re trieved images in order of descending similarity with the query, where the similarity for each image is determined ...
A b stra c t PAS is a picture archival system developed for archiving image documents. ...
In this paper we present an image retrieval system which is based on the probabilistic retrieval model and uses semantic-relations for query formulation. ...
dblp:conf/riao/Al-HawamdehO91
fatcat:pwte4db4dbchzeunskbnys52ya
On image auto-annotation with latent space models
2003
Proceedings of the eleventh ACM international conference on Multimedia - MULTIMEDIA '03
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In this paper, we apply and compare two simple latent space models commonly used in text analysis, namely Latent Semantic Analysis (LSA) and Probabilistic LSA (PLSA). ...
Furthermore, nonprobabilistic methods (LSA and direct image matching) outperformed PLSA on the same dataset. ...
The images used in this study belong to the Corel stock photo collection c . ...
doi:10.1145/957052.957070
fatcat:s3fskg7tl5bvpdjc5vpbyjrcny
Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models
2009
IEEE Transactions on Pattern Analysis and Machine Intelligence
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[1] , in which Bayesian inference in a hierarchical probability model was used to match 3D object models to groupings of curves in a single image. ...
Collections of discriminative local features, when combined with models originally used to learn topics underlying document collections, have recently shown promise for modeling visual object categories ...
doi:10.1109/tpami.2009.160
pmid:19757542
fatcat:xdy2dvqolferbeqib7guqdyfyi
A Fast Matching Method Based on Semantic Similarity for Short Texts
[chapter]
2013
Communications in Computer and Information Science
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The basic idea of SSHash is to directly train a topic model from corpus rather than documents, then project texts into hash codes by using latent features. ...
However, the conventional matching methods suffer from the data sparsity in short documents. In this paper, we propose a novel matching method, referred as semantically similar hashing (SSHash). ...
Maximum likelihood estimation can be used for learning probabilistic general models such as PLSI [14] and LDA [7] . The non-probabilistic models can be reformulated as probabilistic models. ...
doi:10.1007/978-3-642-41644-6_28
fatcat:wmppbbuccze6heprhgjmye4vqy
A Review on Advanced Mechanism in Learning and Recognition of Ops from Weakly Labeled Street View Images
2016
IJARCCE
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Mobile phones are powerful image and video processing device containing the other various features like high-resolution cameras, display color, and hardware-accelerated graphics. ...
On premise sign is a popular form of commercial advertising, widely used in our daily life. The OPSs containing visual diversity associate with complex environmental conditions. ...
They describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis that can be used to automatically learn object models from these ...
doi:10.17148/ijarcce.2016.5130
fatcat:6hja7zef4na4tnjtpylssqynte
Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition
[chapter]
2010
Lecture Notes in Computer Science
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We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. ...
We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. ...
Introduction Graphics recognition deals with graphic entities in document images and is a subfield of document image analysis. ...
doi:10.1007/978-3-642-13728-0_2
fatcat:on3dmxbd7jakdo3ooo6fxwuoni
Probabilistic Topic Models
2010
IEEE Signal Processing Magazine
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Probabilistic topic models are a suite of algorithms whose aim is to discover the hidden thematic structure in large archives of documents. ...
applications towards using topic models for more scientific ends. ...
Summary We have surveyed probabilistic topic models, a suite of algorithms that provide a statistical solution to the problem of managing large archives of documents. ...
doi:10.1109/msp.2010.938079
pmid:25104898
pmcid:PMC4122269
fatcat:pignwt65obhyxinw4b4vfvstxi
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