Detected text‐based image retrieval approach for textual images

Unar, Salahuddin; Wang, Xingyuan; Zhang, Chuan; Wang, Chunpeng

doi:10.1049/iet-ipr.2018.5277

Cited by 44 publications

(14 citation statements)

References 30 publications

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“…Nowadays knowledge is usually described by multimedia documents, which are composed of different media data, such as images and texts. How to accurately understand the contents of these multimedia documents is the key to promote information extraction [1] and retrieval [2]. In these multimedia documents, the data of different media carry complementary information that collectively describe the same object from different perspectives.…”

Section: Introductionmentioning

confidence: 99%

A deep grouping fusion neural network for multimedia content understanding

Song

Shang

et al. 2022

IET Image Processing

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How Deep Neural Networks (DNNs) best cope with the understanding of multimedia contents still remains an open problem, mainly due to two factors. First, conventional DNNs cannot effectively learn the representations of the images with sparse visual information. For example, the images describing knowledge concepts in textbooks. Second, existing DNNs cannot effectively capture the fine‐grained interactions between the images and text descriptions. To address these issues, we propose a deep Cross‐Media Grouping Fusion Network (CMGFN), which mainly has two distinctive properties: 1) CMGFN can effectively learn visual features from the images with sparse visual information. This is achieved by first progressively adjusting the attention of convolution filters to valuable visual regions, and then enhancing the use of key visual information in feature construction. 2) By a cross‐media grouping co‐attention mechanism, CMGFN can effectively use the interactions between visual features of different semantics and textual descriptions, to learn cross‐media features representing different fine‐grained semantics in different groups. Empirical studies demonstrate that CMGFN not only achieves state‐of‐the‐art performance on the multimedia documents containing sparse visual information, but also shows superior general applicability on other multimedia data, e.g., the multimedia fake news.

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Section: Introductionmentioning

confidence: 99%

A deep grouping fusion neural network for multimedia content understanding

Song

Shang

et al. 2022

IET Image Processing

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“…Once this information is obtained, it will be much faster and more accurate to access the images with text search. There are two main frameworks for image retrieval: text‐based image retrieval (TBIR) 8,9 and content‐based image retrieval (CBIR) 10,11 . The text‐based approach can be traced back to the 1970s.…”

Section: Introductionmentioning

confidence: 99%

Figure search by text in large scale digital document collections

Yurtsever

Özcan

Taruz

et al. 2021

Concurrency and Computation

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Summary Digital document collections have been created with the transfer of a large number of documents to digital media. These digital archives have provided many benefits to users. As the diversity and size of digital image collections have grown exponentially, it has become increasingly important and difficult to obtain the desired image from them. The images on the document might contain critical information about the subject of it. In this study, an architecture is developed that can work on large‐scale data by creating regular expressions together with full‐text search approaches. The performance of the system has been tested on different academic documents and Elasticsearch and Apache Solr insert times are compared. Compared to Elasticsearch, Apache Solr achieved faster and more successful results.

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“…Thence, image retrieval technology has been attracting the attention of numerous researchers. Image retrieval technology originated in the 1970s, and at that time, the text-based image retrieval(TBIR) [9]- [12] was the main technology. However, the disadvantage is that it requires manual annotation.…”

Section: Introductionmentioning

confidence: 99%

Color Image Retrieval Algorithm Fusing Color and Principal Curvatures Information

2020

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Aiming at the problems of high dimensional features, poor viewpoint robustness and long retrieval time of existing algorithms in the image retrieval system, this paper presents a new image retrieval algorithm by integrating image color information and surface geometry principal curvatures information. In the proposed method, the color image is first quantized and counted to obtain its color histogram. Simultaneously, the Hessian matrix is used to extract the texture information and the joint histogram of oriented gradient with mix-sampling and multi-scale is constructed. And then, the obtained color histogram and histogram of oriented gradient are fused to obtain the final joint histogram. Experiments are performed on public datasets, and comparison and analysis with representative algorithms based on a single visual feature or set of visual features to verify the performance of our algorithm. The experimental results show that the proposed method has the advantages of low dimensionality, fastness, strong viewpoint robustness and high precision, and can realize image retrieval efficiently.

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Detected text‐based image retrieval approach for textual images

Cited by 44 publications

References 30 publications

A deep grouping fusion neural network for multimedia content understanding

A deep grouping fusion neural network for multimedia content understanding

Figure search by text in large scale digital document collections

Color Image Retrieval Algorithm Fusing Color and Principal Curvatures Information

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