ChartSense

Jung, Daekyoung; Kim, Wonjae; Song, Hyunjoo; Hwang, Jae Yeon; Lee, Bongshin; Kim, Bohyoung; Seo, Jinwook

doi:10.1145/3025453.3025957

Cited by 115 publications

(54 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case an image of the visualization can be captured subsequent to every interaction. The images can be processed using computer vision and machine learning algorithms to extract the properties, such as axis labels or data items [17,26,27,34]. The extracted properties can form a visualization state and/or list of retrieval-relevant properties that are served as input for our retrieval approach (see Figure 5b).…”

Section: Discussion and Limitations 81 Generalizabilitymentioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.

show abstract

Section: Discussion and Limitations 81 Generalizabilitymentioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Their work, named ReVision, achieved 80% accuracy on average for multi-class classification on a 2601 image corpus. Instead of SVM, Jung et al [4] developed a system, named ChartSense, which used a deep learning technique to improve the accuracy rate of ReVision when classifying 10 different types of chart types.…”

Section: Related Workmentioning

confidence: 99%

“…As shown in Table 1, our work differs from the previous works in many respects. First, while many studies [1][2][3][4][5][6][7][8][9] have been focused on the classification of chart images by type (i.e. area, bar, line, pie), our study was conducted to classify line charts according to their trend (increasing or decreasing) and functional (linear or exponential) properties.…”

Section: Related Workmentioning

confidence: 99%

“…area, bar, line, pie), our study was conducted to classify line charts according to their trend (increasing or decreasing) and functional (linear or exponential) properties. Second, in the literature, many studies [1][2][3][4][5][6][7][8][9] have been focused on multi-class classification, while a study [10] conducted the chart type classification as a multi-instance classification problem. Unlike these previous works, our paper presents an experimental study of multi-label classification (MLC) methods (LP and BR) and gives suggestions for MLC that are effective for automatic chart image interpretation applications.…”

Section: Related Workmentioning

confidence: 99%

“…(3) As a data preprocessing step, this paper proposes the application of the Canny edge detection method to increase the classification performance. (4) We also compared two different multi-label classification approaches, called label powerset (LP) and binary relevance (BR), with CNN on the task of line chart classification for the first time. The experimental studies showed that the proposed LP-CNN model achieved 93.75% accuracy on the test set, which contains real-world line chart images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-label classification of line chart images using convolutional neural networks

Kösemen

Birant

2020

SN Appl. Sci.

View full text Add to dashboard Cite

In this paper, we propose a new convolutional neural network (CNN) architecture to build a multi-label classifier that categorizes line chart images according to their characteristics. The class labels are organized in the form of trend property (increasing or decreasing) and functional property (linear or exponential). In the proposed method, the Canny edge detection technique is applied as a data preprocessing step to increase both the classification accuracy and training speed. In addition, two different multi-label solution approaches are compared: label powerset (LP) and binary relevance (BR) methods. The experimental studies show that the proposed LP-CNN model achieves 93.75% accuracy, while the BR-CNN model reaches 92.97% accuracy on the test set, which contains real-world line chart images. The aim of this study is to build an efficient classifier that can be used for many purposes, such as automatically captioning the chart images, providing recommendations, redesigning charts, organizing a collection of chart images and developing better search engines.

show abstract

A technical note on digitizing color mapped spectral power distribution images

Shailesh

2021

Color Research & Application

View full text Add to dashboard Cite

Data visualization is producing images that communicate data in the form of visual objects like lines, points, bars, or colored areas. Often it is necessary to extract numerical data from these images for further analysis. There are a wide variety of data digitization tools, however, only limited formats of plot images can be digitized using them. There exists many data visualization formats spread across different domains of science. Some of these formats need tailored solutions. One such format is color mapped spectral power distribution images of light sources captured by proprietary spectrometers. In this work, a methodology is discussed to digitize spectral plots of light sources. Unlike typical digitization tools which extract data based on contrast difference, color, or morphological similarities, color mapped spectral plots contain blending colors across the x‐axis, without containing any notable morphological features. In this work, image processing is applied to extract the contour representing a spectral plot. For color mapped spectral plots, the importance of appropriate binary input images for edge detection algorithms is highlighted. There are only a few edge‐detection methods give the desired results except a few. Further, the importance of repeatedly adjusting threshold values in extracting desired contours is also discussed. The estimated and measured spectral parameters are in mutual agreement with each other thereby validating the adopted approach.

show abstract

ChartSense

Cited by 115 publications

References 25 publications

KnowledgePearls: Provenance-Based Visualization Retrieval

KnowledgePearls: Provenance-Based Visualization Retrieval

Multi-label classification of line chart images using convolutional neural networks

A technical note on digitizing color mapped spectral power distribution images

Contact Info

Product

Resources

About