What is being Measured in an Information Graphic?

Demir, Şeniz; Schwartz, Stephanie Elzer; Burns, Richard J.; Carberry, Sandra

doi:10.1007/978-3-642-37247-6_40

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Description of data trends may be informed by research on image summaries and captions, which has the goal of expressing the high-level composition or intention of an image that appears in popular media. The goal of such research is not only to convey the meaning of an image to individuals with visual impairments but also to facilitate summarization of multimodal documents and their processing by computer systems [Demir et al , 2013Elzer et al 2005;Feng and Lapata 2010]. This is typically accomplished in two stages.…”

Section: Scatter Plotmentioning

confidence: 99%

Guiding Novice Web Workers in Making Image Descriptions Using Templates

Morash

Siu

Miele

et al. 2015

ACM Trans. Access. Comput.

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This article compares two methods of employing novice Web workers to author descriptions of science, technology, engineering, and mathematics images to make them accessible to individuals with visual and print-reading disabilities. The goal is to identify methods of creating image descriptions that are inexpensive, effective, and follow established accessibility guidelines. The first method explicitly presented the guidelines to the worker, then the worker constructed the image description in an empty text box and table. The second method queried the worker for image information and then used responses to construct a templatebased description according to established guidelines. The descriptions generated through queried image description (QID) were more likely to include information on the image category, title, caption, and units. They were also more similar to one another, based on Jaccard distances of q-grams, indicating that their word usage and structure were more standardized. Last, the workers preferred describing images using QID and found the task easier. Therefore, explicit instruction on image-description guidelines is not sufficient to produce quality image descriptions when using novice Web workers. Instead, it is better to provide information about images, then generate descriptions from responses using templates.

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Section: Scatter Plotmentioning

confidence: 99%

Guiding Novice Web Workers in Making Image Descriptions Using Templates

Morash

Siu

Miele

et al. 2015

ACM Trans. Access. Comput.

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show abstract

“…Authors that are using Hough transformation, Hidden Markov model, vectorization, the histogram of gray color or any other method for obtaining data visualization type [4,5,6] • Authors that are using Bayesian Network (BN) for obtaining data visualization type [2,7,8,9] • Authors that are using Neural Networks (NN) for obtaining data visualization type Some publications stand out in the literature for proposing techniques and methodologies for visualization type classification. The mentioned papers are sorted chronologically from the old to the newer.…”

mentioning

confidence: 99%

Data Visualization Classification Using Simple Convolutional Neural Network Model

Bajić¹,

Job²,

Nenadić³

2020

Int. j. electr. comput. eng. syst. (Online)

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Data visualization is developed from the need to display a vast quantity of information more transparently. Data visualization often incorporates important information that is not listed anywhere in the document and enables the reader to discover significant data and save it in longer-term memory. On the other hand, Internet search engines have difficulty processing data visualization and connecting visualization and the request submitted by the user. With the use of data visualization, all blind individuals and individuals with impaired vision are left out. This article utilizes machine learning to classify data visualizations into 10 classes. Tested model is trained four times on the dataset which is preprocessed through four stages. Achieved accuracy of 89 % is comparable to other methods’ results. It is showed that image processing can impact results, i.e. increasing or decreasing level of details in image impacts on average classification accuracy significantly.

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Review of chart image detection and classification

Bajić

Job

2023

IJDAR

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What is being Measured in an Information Graphic?

Cited by 4 publications

References 10 publications

Guiding Novice Web Workers in Making Image Descriptions Using Templates

Guiding Novice Web Workers in Making Image Descriptions Using Templates

Data Visualization Classification Using Simple Convolutional Neural Network Model

Review of chart image detection and classification

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