Manipulating Bilevel Feature Space for Category-Aware Image Exploration

Mizuno, Kazuyo; Wu, Hsiang‐Yun; Takahashi, Shigeo

doi:10.1109/pacificvis.2014.58

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Recently, several dimensionality reduction methods have appeared in the literature where users can interactively modify visualizations by updating the coordinates of points, or the distances or neighboring relationships between them [PEP*11, MFNP13, MWT14, WTH15]. While these techniques are useful in order to observe relationships between the elements of a data set, they do not show information related to its original attributes.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Adaptable Radial Axes Plots for Improved Multivariate Data Visualization

Rubio-Sánchez

Sánchez

Lehmann

2017

Computer Graphics Forum

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Radial axes plots are multivariate visualization techniques that extend scatterplots in order to represent high‐dimensional data as points on an observable display. Well‐known methods include star coordinates or principal component biplots, which represent data attributes as vectors that define axes, and produce linear dimensionality reduction mappings. In this paper we propose a hybrid approach that bridges the gap between star coordinates and principal component biplots, which we denominate “adaptable radial axes plots”. It is based on solving convex optimization problems where users can: (a) update the axis vectors interactively, as in star coordinates, while producing mappings that enable to estimate attribute values optimally through labeled axes, similarly to principal component biplots; (b) use different norms in order to explore additional nonlinear mappings of the data; and (c) include weights and constraints in the optimization problems for sorting the data along one axis. The result is a flexible technique that complements, extends, and enhances current radial methods for data analysis.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Adaptable Radial Axes Plots for Improved Multivariate Data Visualization

Rubio-Sánchez

Sánchez

Lehmann

2017

Computer Graphics Forum

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“…Coarse semantic information-based methods are proposed to enhance the visualization and exploration. Yang et al [YFH * 06] and Mizuno et al [MWT14] propose to use multidimensional scaling (MDS) [CC08] when computing the similarity between images for visualization, but either keywords are involved in the annotation and search process [YFH * 06] or the resulting embedding is less efficient than t-SNE [MWT14]. MDS was further applied with weights to emphasize items of interest [DWF * 18], while in Projection Explorer for Images [POM07, ENP * 09] (PEx-Image) it was used for image embedding.…”

Section: Related Workmentioning

confidence: 99%

Texture Browser: Feature‐based Texture Exploration

Luo

Scandolo

Eisemann

2021

Computer Graphics Forum

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Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre‐trained convolutional neural network to extract high‐level semantic image features, which are then mapped to a 2‐dimensional location using an adaptation of t‐SNE, a dimensionality‐reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t‐SNE, scalable clustering, and multi‐resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.

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