Kernel Metadata Declaration — on demand

What the DOI identifies

BREAKING NEWS ARTICLE ANNOTATION USING IMAGE AND TEXT PROCESSING

10.46243/jst.2019.v4.i06 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2019-11-01

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 04 · no. 06 · pp. 42–50

Principal agents

• U.GAYATHRI (author → Author)
• M.V.BALARAM (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 15 references. Source: Crossref (member 25296), registered 2026-09-07, last deposited 2026-09-20.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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            "url": "https://www.jst.org.in/index.php/pub/article/download/98/84",
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    "abstract": {
        "value": "Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.",
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            "unstructured": "G. Andrew, R. Arora, J. Bilmes, and K. Livescu. Deep canonical correlation analysis. In ICML, 2013"
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            "doi": "10.1109/iccv.2015.279",
            "unstructured": "Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. VQA: Visual Question Answering. In International Conference on Computer Vision (ICCV), 2015"
        },
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            "unstructured": "K. Barnard, P. Duygulu, D. Forsyth, N. De Freitas, D. Blei, and M. Jordan. Matching words and pictures. The Journal of Ma chine Learning Research, 3:1107–1135, 2003"
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            "doi": "10.1109/iccv.2001.937654",
            "unstructured": "K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In ICCV, volume 2, pages 408–415. IEEE, 2001"
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            "doi": "10.1145/1631272.1631292",
            "unstructured": "L. Cao, J. Yu, J. Luo, and T. Huang. Enhancing semantic and geographic annotation of web images via logistic canonical co rrelation regression. In ACM International Conference on Multimedia, pages 125–134. ACM, 2009"
        },
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            "key": "ref6",
            "doi": "10.3115/v1/w14-3102",
            "unstructured": "A. Chang, M. Savva, and C. Manning. Interactive learning of spatial knowledge for text to 3d scene generation. Sponsor: I dibon, page 14, 2014"
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            "key": "ref7",
            "doi": "10.1109/cvpr.2011.5995610",
            "unstructured": "D. Chen, G. Baatz, K. Köser, S. Tsai, R. Vedantham, T. Pylvä, K. Roimela, X. Chen, J. Bach, M. Pollefeys, et al. City-scale landmark identification on mobile devices. In CVPR, pages 737–744. IEEE, 2011"
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            "key": "ref8",
            "doi": "10.1109/cvpr.2015.7298856",
            "unstructured": "X. Chen and C. Zitnick. Mind’s eye: A recurrent visual representation for image caption generation. In CVPR, 2015"
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            "key": "ref9",
            "doi": "10.1007/978-3-642-28997-2_28",
            "unstructured": "F. Coelho and C. Ribeiro. Image abstraction in crossmedia retrieval for text illustration. Lecture Notes in Computer Scie nce, 7224 LNCS:329–339, 2012"
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            "unstructured": "R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. JMLR, 12(08):2493–2537, 2011"
        },
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            "key": "ref11",
            "doi": "10.1145/383259.383316",
            "unstructured": "B. Coyne and R. Sproat. Wordseye: an automatic text-to-scene conversion system. In Conference on Computer Graphics and Interactive Techniques, pages 487–496. ACM, 2001. [12] D. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world’s photos. In International Conference on World Wide Web, pages 761–770. ACM, 2009"
        },
        {
            "key": "ref12",
            "doi": "10.1109/cvpr.2009.5206848",
            "unstructured": "J. Deng, W. Dong, R. Socher, K. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009"
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            "key": "ref13",
            "doi": "10.1007/s11263-009-0275-4",
            "unstructured": "M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. In ternational journal of computer vision, 88(2):303–338, 2010. [19] H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollar, J. Gao, X. He, M. Mitchell, J. Platt, C. Zitnick, and G. Zweig. From captions to visual concepts and back. In CVPR, 2015. [20] H. Fang, S. Gupt a, F. Iandola, R. Srivastava, L. Deng, and P. Dollár others. From captions to visual concepts and back. In CVPR, pages 1473–1482, 2015"
        },
        {
            "key": "ref14",
            "doi": "10.1007/978-3-642-15561-1_2",
            "unstructured": "A. Farhadi, M. Hejrati, M. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth. Every picture tells a story : Generating sentences from images. In ECCV, pages 15–29. Springer, 2010"
        },
        {
            "key": "ref15",
            "unstructured": "Y. Feng and M. Lapata. Topic Models for Image Annot ation and Text Illustration. Conference of the North American Chapter of the ACL: Human Language Technologies, (June):831–839, 2010"
        }
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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