iMap: a stable layout for navigating large image collections with embedded search

Wang, Chaoli; Reese, John P.; Zhang, Huan; Tao, Jun; Nemiroff, Robert J.

doi:10.1117/12.999313

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…We consider three aspects of images, namely, grayscale content, power spectrum, and color histogram, to calculate the distance between two images. 20 The overall distance between two images is a weighted sum of the three partial distances.…”

Section: I-i Edge K-k Edge and I-k Edgementioning

confidence: 99%

iGraph: a graph-based technique for visual analytics of image and text collections

Wang

et al. 2015

SPIE Proceedings

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In our daily lives, images and texts are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image and text collections. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords, but also supports detailed comparison for understanding and intuitive guidance for navigation. For performance speedup, multiple GPUs and CPUs are utilized for processing and visualization in parallel. We experiment with two image and text collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

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Section: I-i Edge K-k Edge and I-k Edgementioning

confidence: 99%

iGraph: a graph-based technique for visual analytics of image and text collections

Wang

et al. 2015

SPIE Proceedings

Self Cite

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“…Browsing techniques identified were horizontal and vertical browsing, panning, zooming, magnification and scaling. Examples of thumbnail display include the use of 'PileBars' (Brivio et al, 2012), clustering images (Nguyen and Worring, 2006), Treemaps (Wang et al, 2013) and oval bubbles (Smith et al (2006). We use this literature to inform the design of our interface in section 3.…”

Section: Related Work In Image Search Interfacesmentioning

confidence: 99%

Presenting and visualizing results on an image retrieval user interface

Göker

Butterworth

MacFarlane

et al. 2017

Electronic Workshops in Computing

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The volume of images available online has increased significantly but the choices offered by image retrieval systems have not kept pace. We describe the design and evaluation of a 'high density' image search interface focusing on the results pages. We quantitatively and qualitatively compared image presentation on a high density interface with a traditional image search interface. Our results show that users had major problems with high-density interfaces for images due to information overload. We point to further work which could improve the user experience.

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“…Brivio et al 9 presented dynamic image browsing which partitions the screen with weighted Voronoi diagrams to visualize images of different sizes, orientations, and aspect ratios. Wang et al 10 designed iMap, a treemap-based representation for visualizing and navigating image clustering and search results. Their layout places the query image at the center of the display area and arranges other images based on image similarity along a spiral shape from inside out.…”

Section: Visualization Of Image and Text Collectionsmentioning

confidence: 99%

Visualization and recommendation of large image collections toward effective sensemaking

Wang

et al. 2016

Information Visualization

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In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

show abstract

iMap: a stable layout for navigating large image collections with embedded search

Cited by 14 publications

References 23 publications

iGraph: a graph-based technique for visual analytics of image and text collections

iGraph: a graph-based technique for visual analytics of image and text collections

Presenting and visualizing results on an image retrieval user interface

Visualization and recommendation of large image collections toward effective sensemaking

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