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22nd Text REtrieval Conference, TREC 2013

作者： Roy, Dwaipayan Bandyopadhyay, Ayan Mitra, Mandar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Contextual Suggestion Problem focuses on search techniques for complex information needs that are highly dependent on context and user interest. In this paper, we present our approach to providing user and context dependent suggestions. The official evaluation scores for our submitted run are reported and compared to the overall median scores (across all 34 runs). © 2013 22nd Text REtrieval Conference, TREC 2013 - Proceedings. All Rights Reserved.

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Extraction of type style based meta-information from imaged documents 5

Extraction of type style based meta-information from imaged ...

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5th International Conference on Document Analysis and recognition, ICDAR 1999

作者： Garain, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 Barrackpore Trunk Road Calcutta700 035 India

ISBN: (纸本)0769503187

Extraction of some meta-information from printed documents without an OCR approach is considered. It can be statistically verified that important terms in articles are printed in italic, bold and all capital style. Detection of these type styles helps in automatic extraction of the lines containing titles, authors' names, subtitles, references as well as sentences having important terms occurring in the text. It also helps in improving the OCR performance for reading the italic text. Some experimental results on the performance of the approach on good quality as well as degraded document images are presented. © 1999 IEEE.

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Building test collection from Old IR literature 14

Building test collection from Old IR literature

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6th International Workshop of the Forum for Information Retrieval Evaluation, FIRE 2014

作者： Chakraborty, Anirban Ghosh, Kripabandhu Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

ISBN: (纸本)9781450337557

Standard test collections form the very basis of Information Retrieval research and evaluation. Important datasets have been created to promote empirical research and experimentation. In this paper, we describe our endeavour in creating a test collection from old, archived writings of IR stalwarts. The documents are created in text format from the scanned and OCRed version. The test collection consists of a set of documents in TREC format along with a set of expert queries and their relevance assessments. This dataset, though small in size, would be of paramount interest for researchers and students of IR since it contains valuable discourses on the discipline from its very inception. Also, to the best of our knowledge, no standard IR dataset has been built so far comprising old research articles. Furthermore, this is a dataset without the original error-free digital text version. So, the resulting collection would expect researchers to run retrieval experiments on the erroneous collection without the scope of error modeling. This would invite new research ideas. © 2015 ACM.

关键词： Errors

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A new multimodal sentiment analysis for images containing textual information

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Multimedia Tools and Applications 2024年 1-30页

作者： Ahuja, Garvit Alaei, Alireza Pal, Umapada School of Computer Science and Engineering Manipal University Jaipur Jaipur India Faculty of Science and Engineering Southern Cross University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images and text (reviews) has its own challenges, the combination of textual and visual content in the form of images presents new challenges as well as opportunities. In this research work, we proposed a multimodal sentiment analysis method that works on images incorporating textual elements. In the textual sentiment analysis model, we initially employed a recognition system to extract textual data from input images. Our proposed multimodal method is based on transfer learning, considering two pre-trained deep learning models, Xception, and RoBERTa, to extract features from both visual and textual content from multimedia images. We then implemented a fusion strategy to combine these two modalities (Visual Sentiment Analysis (VSA) and Textual Sentiment Analysis (TSA)) to enhance the accuracy of the proposed method and to provide a more comprehensive understanding of sentiment in multimedia content. In addition, we curated a custom dataset comprising images with associated text labels and sentiments. To ensure accurate labels, we conducted human evaluations involving thirty annotators. Our dataset includes images labeled with negative, neutral, and positive sentiments. Experimental results demonstrated the effectiveness of combining visual and textual features for sentiment analysis. The findings from this research hold promising implications for real-world applications, such as sentiment analysis in social media, product reviews, and marketing campaigns, where both images and text play a significant role in conveying emotional context. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Offline handwritten Devanagari word recognition: An HMM based approach

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2nd International Conference on pattern recognition and Machine Intelligence, PReMI 2007

作者： Parui, Swapan Kumar Shaw, Bikash Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India

ISBN: (纸本)3540770453

A hidden Markov model (HMM) for recognition of handwritten Devanagari words is proposed. The HMM has the property that its states are not defined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word is assumed to be a string of several stroke primitives. These are in fact the states of the proposed HMM and are found using certain mixture distributions. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The classification scheme has been tested on a small handwritten Devanagari word database developed recently. The classification accuracy is 87.71% and 82.89% for training and test sets respectively. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Word processing

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TREC 2020 NEWS Track Background Linking Task 29

TREC 2020 NEWS Track Background Linking Task

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29th Text REtrieval Conference, TREC 2020

作者： Gautam, Rahul Mitra, Mandar Roy, Dwaipayan Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Background Linking task is a problem that focuses on providing users with suggestions for articles to read next, when the user is reading a news article. The suggested articles should provide adequate context and background information for the article that the user is currently reading. In this paper, we describe several methods that we explored for this task, and report their results. © 2020 29th Text REtrieval Conference, TREC 2020 - Proceedings. All Rights Reserved.

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Multi-label learning on principles of reverse k-nearest neighbourhood

Multi-label learning on principles of reverse k-nearest neig...

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作者： Sadhukhan, Payel Palit, Sarbani Machine Intelligence Unit Indian Statistical Institute Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, we present a novel neighbourhood based multi-label classifier, Multi-label Learning on principles of Reverse k-Nearest Neighbourhood (ML-RkNN) where we estimate the neighbourhood of the points on the basis of their reverse k-nearest neighbourhood (RkNN). Through RkNN, for the same value of k, we get different number of neighbours for different instances and this happens adaptively according to the neighbourhood configuration of the points. The automatically adaptive neighbourhood helps us in better learning of the local configurations around the points. Our scheme also facilitates implicit handling of the local imbalances prevailing in the datasets by comparing the class distributions of the test points and their reverse nearest neighbours. This implicit and adaptive handling is particularly useful for multi-label label datasets, whose labels are differentially imbalanced. Empirical study is performed on 10 real-world multi-label datasets considering five neighbourhood based multi-label learners. Macro F1 is used as the evaluating metric. The proposed method has given statistically superior and statistically comparable performances with respect to three and two comparing methods respectively. Additionally, we have explored the use of two different distance metrics, Euclidean and Jaccard in our scheme for nominal datasets. © 2020 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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ISI at the Sigmorphon 2017 shared task on morphological reinflection

ISI at the Sigmorphon 2017 shared task on morphological rein...

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2017 CoNLL SIGMORPHON Shared Task: Universal Morphological Reinflection, CoNLL 2017

作者： Chakrabarty, Abhisek Garain, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

ISBN: (纸本)9781945626692

We present a system for morphological reinflection based on the LSTM model. Given an input word and morphosyntactic descriptions, the problem is to classify the proper edit tree that, applied on the input word, produces the target form. The proposed method does not require human defined features and it is language independent also. Currently, we evaluate our system only for task 1 without using any external data. From the test set results, it is found that the proposed model beats the baseline on 15 out of the 52 languages in high resource scenario. But its performance is poor when the training set size is medium or low. © 2017 Association for Computational Linguistics.

关键词： Long short-term memory

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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769546612

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices. © 2012 IEEE.

关键词： Support vector machines

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An efficient algorithm for detection of road-like structures in satellite images

An efficient algorithm for detection of road-like structures...

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13th International Conference on pattern recognition, ICPR 1996

作者： Mukherjee, A. Parui, S.K. Chaudhuri, D. Chaudhuri, B.B. Krishnan, R. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India Advanced Data Processing Research Institute Secunderabad India

ISBN: (纸本)081867282X

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory. © 1996 IEEE.

关键词： Satellite imagery

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