A Salience‐based Quality Metric for Visualization

Jänicke, Heike; Chen, M.

doi:10.1111/j.1467-8659.2009.01667.x

Cited by 47 publications

(41 citation statements)

References 35 publications

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“…In contrast, Filonik and Baur [20] considered the aesthetics of a visualization as a quality metric. Recently, Jänicke and Chen [21] proposed a salience-based metric to evaluate visualizations in general, but also used flow visualizations including a LIC image as a specific example. They evaluate the quality of an image by comparing a salience map with a relevance map that is either user defined or computed from the visualization data with a salience map.…”

Section: Previous Workmentioning

confidence: 99%

A Metric for the Evaluation of Dense Vector Field Visualizations

Matvienko

Krüger

2013

IEEE Trans. Visual. Comput. Graphics

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In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric’s applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.

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Section: Previous Workmentioning

confidence: 99%

A Metric for the Evaluation of Dense Vector Field Visualizations

Matvienko

Krüger

2013

IEEE Trans. Visual. Comput. Graphics

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“…The saliency model has been used to analyze the effectiveness of a visual representation. For example, Jänicke and Chen proposed a salience-based quality metric for visualization using the correspondence between the data relevance mask and the saliency map [7]. Although there have been some works on analyzing the quality of a visual representation based on saliency, there is still no good way to automatically generate the visual representation of data which is saliency-maximized.…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving faster-than-real-time human acoustic event detection by saliency-maximized audio visualization

Lin

Zhuang

Goudeseune

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a saliency-maximized audio spectrogram as a representation that lets human analysts quickly search for and detect events in audio recordings. By rendering target events as visually salient patterns, this representation minimizes the time and effort needed to examine a recording. In particular, we propose a transformation of a conventional spectrogram that maximizes the mutual information between the spectrograms of isolated target events and the estimated saliency of the overall visual representation. When subjects are shown spectrograms that are saliency-maximized, they perform significantly better in a 1/10-real-time acoustic event detection task.

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“…This provides an application-independent, purely mathematical measurement of information. More recently, a very interesting and expansive treatment of how saliency and information theory can be adapted and adopted for visualization has been carried out by Chen and Jänicke [6,18]. In this paper we wish to slightly side-step the deeply intriguing topic of how to quantify visual information content of a region in the general case, and instead talk about what we have found to work well for large-scale landscape images.…”

Section: Information Discoverymentioning

confidence: 99%

Saliency-Assisted Navigation of Very Large Landscape Images

Varshney

2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-The field of visualization has addressed navigation of very large datasets, usually meshes and volumes. Significantly less attention has been devoted to the issues surrounding navigation of very large images. In the last few years the explosive growth in the resolution of camera sensors and robotic image acquisition techniques has widened the gap between the display and image resolutions to three orders of magnitude or more. This paper presents the first steps towards navigation of very large images, particularly landscape images, from an interactive visualization perspective. The grand challenge in navigation of very large images is identifying regions of potential interest. In this paper we outline a three-step approach. In the first step we use multi-scale saliency to narrow down the potential areas of interest. In the second step we outline a method based on statistical signatures to further cull out regions of high conformity. In the final step we allow a user to interactively identify the exceptional regions of high interest that merit further attention. We show that our approach of progressive elicitation is fast and allows rapid identification of regions of interest. Unlike previous work in this area, our approach is scalable and computationally reasonable on very large images. We validate the results of our approach by comparing them to user-tagged regions of interest on several very large landscape images from the Internet.

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A Salience‐based Quality Metric for Visualization

Cited by 47 publications

References 35 publications

A Metric for the Evaluation of Dense Vector Field Visualizations

A Metric for the Evaluation of Dense Vector Field Visualizations

Improving faster-than-real-time human acoustic event detection by saliency-maximized audio visualization

Saliency-Assisted Navigation of Very Large Landscape Images

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