Document Image Retrieval Using Feature Combination in Kernel Space
23 Aug 2010-pp 2009-2012
TL;DR: A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed and the Genetic Algorithm based framework is used for optimization.
Abstract: The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented.
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TL;DR: Various feature extraction and classification techniques associated with the OSI of the Indic scripts are discussed in this survey and it is hoped that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India.
Abstract: Offline Script Identification (OSI) facilitates many important applications such as automatic archiving of multilingual documents, searching online/offline archives of document images and for the selection of script specific Optical Character Recognition (OCR) in a multilingual environment. In a multilingual country like India, a document containing text words in more than one language is a common scenario. A state-of-the-art survey about the techniques available in the area of OSI for Indic scripts would be of a great aid to the researchers. Hence, a sincere attempt is made in this article to discuss the advancements reported in the literature during the last few decades. Various feature extraction and classification techniques associated with the OSI of the Indic scripts are discussed in this survey. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indic scripts together. Taking the recent developments in OSI of Indian regional scripts into consideration, this article will provide a better platform for future research activities.
42 citations
Cites methods from "Document Image Retrieval Using Feat..."
...posed in [36] has been applied for word image representation....
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17 Sep 2011
TL;DR: The proposed framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages by utilizing texture and shape based information embedded in the documents at different levels for feature extraction.
Abstract: Script identification in a multi-lingual document environment has numerous applications in the field of document image analysis, such as indexing and retrieval or as an initial step towards optical character recognition. In this paper, we propose a novel hierarchical framework for script identification in bi-lingual documents. The framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages. We utilize texture and shape based information embedded in the documents at different levels for feature extraction. The prediction task at different levels of hierarchy is performed by Support Vector Machine (SVM) and Rejection based classifier defined using AdaBoost. Experimental evaluation of the proposed concept on document collections of Hindi/English and Bangla/English scripts have shown promising results.
12 citations
References
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01 Dec 2001
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Abstract: From the Publisher:
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
7,880 citations
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
6,693 citations
Additional excerpts
...fundamentally based on shape context [7]....
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Journal Article•
3,076 citations
"Document Image Retrieval Using Feat..." refers methods in this paper
...The kernel methods increase the computational power of linear learning algorithms by mapping the data to high dimensional feature space [6]....
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Journal Article•
TL;DR: It is shown that the proposed multiple kernel learning algorithm can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations, and generalize the formulation and the method to a larger class of problems, including regression and one-class classification.
Abstract: While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun .
1,367 citations
"Document Image Retrieval Using Feat..." refers background in this paper
...The MKL have shown significant improvement in many classification problems by learning the optimal combination of different data sources, or by learning the optimal kernel for the classifier from the data itself [2]– [4]....
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01 Sep 2009
TL;DR: Several models that aim at learning the correct weighting of different features from training data are studied, including multiple kernel learning as well as simple baseline methods and ensemble methods inspired by Boosting are derived.
Abstract: A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture. In this paper we study several models that aim at learning the correct weighting of different features from training data. These include multiple kernel learning as well as simple baseline methods. Furthermore we derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.
898 citations
"Document Image Retrieval Using Feat..." refers background in this paper
...The MKL have shown significant improvement in many classification problems by learning the optimal combination of different data sources, or by learning the optimal kernel for the classifier from the data itself [2]– [4]....
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