Automatic Image Description Based on Textual Data

Badr, Youakim; Chbeir, Richard

doi:10.1007/11890591_7

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…In recent years, establishing frameworks to solve problems in specific areas has become popular. For example, Badr and Chbeir (2006) worked on a framework for the medical field, whereas Wang et al (2014) focussed on cultural heritage areas. As ontology has also been widely used in the construction of hierarchical description frameworks, some studies (James et al , 2010) also proposed an ontology-matching method in image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Towards a semantics representation framework for narrative images

Wang

et al. 2019

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Purpose The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process. Design/methodology/approach This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco. Findings The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model. Research limitations/implications ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources. Originality/value This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.

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Section: Related Workmentioning

confidence: 99%

Towards a semantics representation framework for narrative images

Wang

et al. 2019

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Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Automatic Image Description Based on Textual Data

Cited by 2 publications

References 32 publications

Towards a semantics representation framework for narrative images

Towards a semantics representation framework for narrative images

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

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