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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Ghosh, Kripabandhu Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

ISBN: (纸本)9781450337946

In relevance feedback, first-round search results are used to boost second-round search results. Two forms have been traditionally considered: exhaustively labelled feedback, where all first-round results to depth k arc annotated for relevance by the user;and blind feedback, where the top-k results arc all assumed to be relevant. In this paper, we consider an intermediate, semi-supervised scheme, in which only a subset of results is selected for annotation, and then their labels are propagated to their nearest neighbours. Specifically, we use clustering to determine the nearest-neighbour groups, and seed selection to choose documents for annotation. We find that the effectiveness of this method is indistinguishable from the exhaustive relevance feedback, and is significantly higher than both blind feedback and the use of the annotated subset alone. We show that this approach works well in environments in which some but limited amounts of human feedback arc available, such as early case assessment in e-discovcry. © 2015 ACM.

关键词： relevance assessment cluster hypothesis e-discovery

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An Investigation of Novel Combined Features for a Handwritten Short Answer Assessment System

An Investigation of Novel Combined Features for a Handwritte...

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International Workshop on Frontiers in Handwriting recognition

作者： Hemmaphan Suwanwiwat Umapada Pal Michael Blumenstein College of Business Law and Governance (Information Technology) James Cook University Australia Computer Vision and Pattern Recognition Unit Indian Institute India School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509009824

This paper proposes an off-line automatic assessment system utilising novel combined feature extraction techniques. The proposed feature extraction techniques are 1) the proposed Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (WRL_MDGGF), 2) the proposed Gravity, Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (G_WRL_MDGGF). The proposed feature extraction techniques together with their original features and other combined feature extraction techniques were employed in an investigation of the efficiency of feature extraction techniques on an automatic off-line short answer assessment system. The proposed system utilised two classifiers namely, artificial neural networks and Support Vector Machines (SVMs), two type of datasets and two different thresholds in this investigation. Promising recognition rates of 94.85% and 94.88% were obtained when the proposed WRL_MDGGF and G_WRL_MDGGF were employed, respectively, using SVMs.

关键词： Feature extraction Gravity Training Reservoirs Testing Support vector machines

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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture recognition

ChaLearn Looking at People RGB-D Isolated and Continuous Dat...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Jun Wan Stan Z. Li Yibing Zhao Shuai Zhou Isabelle Guyon Sergio Escalera National Laboratory of Pattern Recognition Chinese Academy of Sciences China Macau University of Science and Technology Macau UPSud and INRIA Université Paris-Saclay ChaLearn University of Barcelona Computer Vision Center ChaLearn

In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented.

关键词： Gesture recognition Training Indexes computer vision Testing Conferences

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New Tampered Features for Scene and Caption Text Classification in Video Frame

New Tampered Features for Scene and Caption Text Classificat...

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International Workshop on Frontiers in Handwriting recognition

作者： Sangheeta Roy Palaiahnakote Shivakumara Umapada Pal Tong Lu Chew Lim Tan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore

ISBN: (纸本)9781509009824

The presence of both caption/graphics/superimposed and scene texts in video frames is the major cause for the poor accuracy of text recognition methods. This paper proposes an approach for identifying tampered information by analyzing the spatial distribution of DCT coefficients in a new way for classifying caption and scene text. Since caption text is edited/superimposed, which results in artificially created texts comparing to scene texts that exist naturally in frames. We exploit this fact to identify the presence of caption and scene texts in video frames based on the advantage of DCT coefficients. The proposed method analyzes the distributions of both zero and non-zero coefficients (only positive values) locally by moving a window, and studies histogram operations over each input text line image. This generates line graphs for respective zero and non-zero coefficient coordinates. We further study the behavior of text lines, namely, linearity and smoothness based on centroid location analysis, and the principal axis direction of each text line for classification. Experimental results on standard datasets, namely, ICDAR 2013 video, 2015 video, YVT video and our own data, show that the performances of text recognition methods are improved significantly after-classification compared to before-classification.

关键词： Discrete cosine transforms Text recognition Image color analysis Linearity Bars Character recognition Mathematical model

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Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning

arXiv

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arXiv 2017年

作者： Aubreville, Marc Knipfer, Christian Oetter, Nicolai Jaremenko, Christian Rodner, Erik Denzler, Joachim Bohr, Christopher Neumann, Helmut Stelzle, Florian Maier, Andreas Pattern Recognition Lab Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Medical Center Hamburg-Eppendorf Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Medical Center Erlangen Friedrich-Alexander- Universität Erlangen-Nürnberg Germany Computer Vision Group Friedrich-Schiller-Universität Jena Germany Department of Otorhinolaryngology Head and Neck Surgery University Medical Center Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Germany First Department of Internal Medicine University Medical Center Mainz Johannes Gutenberg-Universität Mainz Germany

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and an reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for a highly accurate OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3 % (sensitivity 86.6 %, specificity 90 %). Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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CNN based common approach to handwritten character recognition of multiple scripts 13

CNN based common approach to handwritten character recogniti...

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13th International Conference on Document Analysis and recognition, ICDAR 2015

作者： Maitra, Durjoy Sen Bhattacharya, Ujjwal Parui, Swapan K. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-108 India

ISBN: (纸本)9781479918058

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official Indian script. recognition accuracies are comparable with the state-of-the-art. © 2015 IEEE.

关键词： Character recognition

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New Sharpness Features for Image Type Classification Based on Textual Information

New Sharpness Features for Image Type Classification Based o...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： K. S. Raghunandan Palaiahnakote Shivakumara G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Narional Key Lab for Novel Software Technology Nanjing University Nanjing China

Achieving good recognition results from a single method for text lines in video/natural scene images captured by high resolution cameras or low resolution mobile cameras, and images in web pages, is often hard. In this paper, we propose new sharpness based features of textual portion of each input text line image using HSI color space for the classification of an input image into one of the four classes (video, scene, mobile or born digital). This helps in choosing an appropriate method based on the class type of the input text for its improved recognition rate. For a given input text line image, the proposed method obtains H, S and I images. Then Canny edge images are obtained for H, S and I spaces, which results in text candidates. We perform sliding window operation over the text candidate image of each text line of each color space to estimate new sharpness by calculating stroke width and gradient information. The sharpness values of the text lines of the three color spaces are then fed to k-means clustering with maximum, minimum and average guesses, which results in three respective clusters. The mean of each cluster for respective color spaces outputs a feature vector having nine feature values for image classification with the help of an SVM classifier. Experimental results on standard datasets, namely, ICDAR 2013, ICDAR 2015 video, ICDAR 2015 natural scene data, ICDAR 2013 born digital data and the images captured by a mobile camera (our own data) show that the proposed classification method helps in improving recognition results.

关键词： Mobile communication Text recognition Image edge detection Image resolution Image color analysis Digital images Optical character recognition software

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Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis

Fourier Coefficients for Fraud Handwritten Document Classifi...

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International Workshop on Frontiers in Handwriting recognition

作者： K.S. Raghunandan Palaiahnakote Shivakumara B.J. Navya G. Pooja Navya Prakash G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781509009824

As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate.

关键词： Image reconstruction Image edge detection Ink Printers Forgery Distortion Feature extraction

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Using electromagnetic input for multi-user or two-handed spatial gestural interaction based on the digital compass 15

Using electromagnetic input for multi-user or two-handed spa...

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17th International Conference on Human-computer Interaction with Mobile Devices and Services, MobileHCI 2015

作者： Yuksel, Kamer Ali Baz, Ipek Ozduman, Haluk Computer Vision and Pattern Recognition Laboratory Sabanci University Turkey Research Center for ICT German Turkish Advanced Technical University of Berlin Germany

ISBN: (纸本)9781450336529

Multiple researchers recently proposed the use of the digital compass embedded in mobile devices for touchless interaction in the 3D space around them. These methods overcome several limits imposed by other interaction techniques and were evaluated for a variety of uses. However, they do not support collaborative settings and are prone to dynamic noise caused by external conditions, as with most other sensor-based interaction techniques. In this paper, we propose the use of frequency-modulated electromagnets as an input medium for magnetic interaction to overcome its various constraints and further enable multi-user and two-handed input. Furthermore, we demonstrated the hardware design specifications of a novel input device, referred to as electromagnetic stylus, which is prototyped to conduct a user-study on the proposed method. Experimental results indicate that gestures performed simultaneously by four electromagnetic styli can accurately be recognized using a single magnetic field sensor, and dynamic noises can be substantially reduced. © 2015 ACM.

关键词： Timing circuits

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SSRBC 2016: Sclera Segmentation and recognition Benchmarking Competition

SSRBC 2016: Sclera Segmentation and Recognition Benchmarking...

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IAPR International Conference on Biometrics (ICB)

作者： Abhijit Das Umapada Pal Miguel A. Ferrer Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509018703

This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.

关键词： Image segmentation Clustering algorithms Iris recognition Manuals Feature extraction

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