ImageMap - Visually Browsing Millions of Images

Barthel, Kai Uwe; Hezel, Nico; Mackowiak, Radek

doi:10.1007/978-3-319-14442-9_30

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Sharing the same large-scale dataset 6 and evaluating each task simultaneously in one room, Video Browser Showdown [24,25,32] Poster Session B2: Deep Learning for Multimedia & Emerging Multimedia Applications MM '20, October 12-16, 2020, Seattle, WA, USA provides a reputable platform for comparative evaluations of various models and interactive search strategies. Each team participates with two tools/users cooperating on solving the currently evaluated task.…”

Section: Vbs 2020 Competition: Settings and Resultsmentioning

confidence: 99%

“…Interactive searching can be also based on visual browsing, where a candidate set of images can be organized into an exploration structure. Such strategy was integrated into HTW [6] and diveXplore [16] systems that used an image sorting approach to organize images to a 2D grid. On top of such grid, a hierarchical structure was constructed, enabling browsing with different levels of granularity.…”

Section: Interactive Search Systemsmentioning

confidence: 99%

“…VIRET used keyword search using class labels and (transformed) confidence scores assigned by a deep convolutional neural network 5. Matching keywords are "visually" prompted with their top matching images 6. Currently, the V3C1 dataset with 1000 hours of videos is used[33].…”

mentioning

confidence: 99%

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A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval

Lokoč

Souček

Veselý

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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As reported by respected evaluation campaigns focusing both on automated and interactive video search approaches, deep learning started to dominate the video retrieval area. However, the results are still not satisfactory for many types of search tasks focusing on high recall. To report on this challenging problem, we present two orthogonal task-based performance studies centered around the state-of-the-art W2VV++ query representation learning model for video retrieval. First, an ablation study is presented to investigate which components of the model are effective in two types of benchmark tasks focusing on high recall. Second, interactive search scenarios from the Video Browser Showdown are analyzed for two winning prototype systems implementing a selected variant of the model and providing additional querying and visualization components. The analysis of collected logs demonstrates that even with the state-of-the-art text search video retrieval model, it is still auspicious to integrate users into the search process for task types, where high recall is essential. CCS CONCEPTS • Information systems → Video search.

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Section: Vbs 2020 Competition: Settings and Resultsmentioning

confidence: 99%

Section: Interactive Search Systemsmentioning

confidence: 99%

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A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval

Lokoč

Souček

Veselý

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…Wikiview 6 Barthel et al [2019] uses Self-Sorting Maps (SSM) Strong and Gong [2014] for visual sorting to allow browsing of Wikimedia Common images. Picsbuffet 7 Barthel et al [2015] can be used to explore images from Pixabay, Fotolia, and IKEA.…”

Section: Related Workmentioning

confidence: 99%

PicArrange -- Visually Sort, Search, and Explore Private Images on a Mac Computer

Jung¹,

Barthel²,

Hezel³

et al. 2021

Preprint

Self Cite

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The native macOS application PicArrange integrates state-of-the-art image sorting and similarity search to enable users to get a better overview of their images. Many file and image management features have been added to make it a tool that addresses a full image management workflow. A modification of the Self Sorting Map algorithm enables a list-like image arrangement without loosing the visual sorting. Efficient calculation and storage of visual features as well as the use of many macOS APIs result in an application that is fluid to use.

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“…The ColorMap (Fig. 2) clusters all images into color classes using Self Organizing Maps for image visualization and offers fast browsing in the multimedia database [12], [13]. Using Self Organizing Maps [12], all images are clustered, hence, all images of the collection are organized by color.…”

Section: Colormap Clustering Modulementioning

confidence: 99%

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

Moumtzidou

Gialampoukidis

Mironidis

et al. 2016

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)

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This paper presents an interactive multimedia search engine, which is capable of searching into multimedia collections by fusing textual and visual information. Apart from multimedia search, the engine is able to perform text search and image retrieval independently using both high-level and lowlevel information. The images of the multimedia collection are organized by color, offering fast browsing in the image collection.

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ImageMap - Visually Browsing Millions of Images

Cited by 10 publications

References 7 publications

A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval

A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval

PicArrange -- Visually Sort, Search, and Explore Private Images on a Mac Computer

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

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