CAT: A Hierarchical Image Browser Using a Rectangle Packing Technique

Gomi, Ai; Miyazaki, Reiko; Itoh, Takayuki; Li, Jia

doi:10.1109/iv.2008.8

Cited by 59 publications

(24 citation statements)

References 11 publications

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“…The multidimensional scaling layout based on semantic similarities was used for image overview and the value and relation layout was used for content overview. Gomi et al 13 presented clustered album thumbnails (CAT) for hierarchical browsing large image collections which shows representative images when zooming out and individual images when zooming in.…”

Section: Image Collection Organizationmentioning

confidence: 99%

Similarity-based visualization of large image collections

Wang

Reese

Zhang

et al. 2013

Information Visualization

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Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. Targeting an online astronomy archive with thousands of images, we present our solution for image search and clustering based on the evaluation of image similarity using both visual and textual information. Time-consuming image similarity computation is accelerated using GPU. To lay out images, we introduce iMap, a treemap-based representation for visualizing and navigating image search and clustering results. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. We also develop an embedded visualization that integrates image tags for in-place search refinement. To show the effectiveness of our approach, we demonstrate experimental results, compare our iMap layout with a force-directed layout, and conduct a comparative user study. As a potential tool for astronomy education and outreach, we deploy our iMap to a large tiled display of nearly 50 million pixels.

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Section: Image Collection Organizationmentioning

confidence: 99%

Similarity-based visualization of large image collections

Wang

Reese

Zhang

et al. 2013

Information Visualization

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“…Clustering-based visualisations group visually similar images together, often in a hierarchical manner. Among the systems that employ such an approach are [14][15][16][17][18][19]. In graph-based navigation systems, images are the nodes of a graph structure, while the edges of the graph show relationships between images (e.g., joint keywords or visual similarity).…”

Section: Image Database Browsingmentioning

confidence: 99%

Intuitive Image Database Browsing to Support Image Annotation

Schaefer

Stuttard²

2012

Automatika

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Original scientific paperImage annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system. Key words:Visual information processing, Image databases, Image browsing, Image annotation, Hue sphere.Intuitivno pretraživanje baze slike kao potpora označavanju slika. Označavanje slika obično se obavlja ručno jer automatski pristupi još nisu dovoljno kvalitetni kako bi se koristili u praksi. Zbog toga je označavanje slika u bazi vremenski zahtjevno. U ovom radu pokazatćemo kako se sustav za pregled slika u bazi može koristiti kao učinkovita potpora ručnom označavanju slika. Za razliku od drugih pristupa, koji prikazuju slike u linearnom poretku, korištena je vizualizacija u kojoj su slike složene po medusobnoj sličnosti. Budući da su na taj način slične slike medusobno blizu jedna drugoj, lako ih je selektirati i zajednički označiti. Slike su organizirane u mrežni prikaz radi sprječavanja preklapanja i jasnije prezentacije. Velike baze podataka organizirane su u hijerarhijsku strukturu gdje svaka slika u pojedinoj vizualizaciji može pripadati skupu slikačiji prikaz korisnik po želji može proširivati. Rezultati provedenih eksperimenata pokazuju da se označavanje slika pomoću predloženog sustava može obavljati brže nego na uobičajeni način.Ključne riječi: obrada vizualnih informacija, baze slika, pretraživanje slika, označavanje slika

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“…We asked them to search for specic images specied by various conditions, and measured the time it took them to locate the photographs. We compared LTView and an existing 2D image browser [4], and demonstrated that subjects took 3.5 times longer in average when using the existing technique, and 4 times longer in maximum.…”

Section: Examplesmentioning

confidence: 99%

“…Kustanowith et al presented a technique [12] that radially places clusters of images, and provides focus+context representation. We presented an image browser CAT [4], that represents clusters of images as nested rectangular regions. We also presented MIAOW, which displays location-and time-based clusters of photographs as nested rectangular regions [3].…”

Section: Image Browsingmentioning

confidence: 99%

A personal photograph browser for life log analysis based on location, time, and person

Gomi

Itoh

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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Image browsers are important and useful applications for retrieving images from personal photograph collections. Such browsers can be a life log analysis tool to explore the events of photograph owners. This paper presents a novel photograph browser consisting of two linked views. One of the views displays photographs clustered based on their locations and times, and the other displays people clustered based on their co-occurrences in the events. Specifying a photograph, the corresponding time the picture was shot and people in the photograph are highlighted. Specifying a time, corresponding photographs and people are highlighted. Specifying a person, associated photographs are highlighted, and their corresponding times are shown. The mechanism helps users to discover interested photographs and understand the events of photograph owners. This paper presents a real scenario and user experiment, demonstrating the eectiveness of the presented browser.

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CAT: A Hierarchical Image Browser Using a Rectangle Packing Technique

Cited by 59 publications

References 11 publications

Similarity-based visualization of large image collections

Similarity-based visualization of large image collections

Intuitive Image Database Browsing to Support Image Annotation

A personal photograph browser for life log analysis based on location, time, and person

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