Color image (dis)similarity assessment and grouping based on dominant colors

Karasek, Jan; Bürget, Radim; Uher, Václav; Masek, Jan; Dutta, Malay Kishore

doi:10.1109/tsp.2015.7296366

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…We also described method based on dominant colours in [15] for measuring image similarity and in [16] system for automatic image labelling using similarity measures is described. In [17] video scenes were segmented using similarity measures.…”

Section: Related Workmentioning

confidence: 99%

Image Search Using Similarity Measures Based on Circular Sectors

Masek

Bürget

Povoda

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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With growing number of stored image data, image search and image similarity problem become more and more important. The answer can be solved by Content-Based Image Retrieval systems. This paper deals with an image search using similarity measures based on circular sectors method. The method is inspired by human eye functionality. The main contribution of the paper is a modified method that increases accuracy for about 8% in comparison with original approach. Here proposed method has used HSB colour model and median function for feature extraction. The original approach uses RGB colour model with mean function. Implemented method was validated on 10 image categories where overall average precision was 67%.

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Section: Related Workmentioning

confidence: 99%

Image Search Using Similarity Measures Based on Circular Sectors

Masek

Bürget

Povoda

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

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“…The Distant Viewing Toolkit addresses these needs by (1) constructing an object-oriented framework for applying a collection of algorithms to moving images, (2) packaging together common sound and computer vision algorithms in order to provide out-of-the-box functionality for common tasks in the computational analysis of moving images, and (3) allowing video files alongside still images as an input. Currently provided algorithms include functionality for: shot detection (Pal et al, 2015), object detection (Li, Zhao, & Zhang, 2018), face detection (Jiang & Learned-Miller, 2017), face identification (Cao, Shen, Xie, Parkhi, & Zisserman, 2018), color analysis (Karasek, Burget, Uher, Masek, & Dutta, 2015), image similarity (Szegedy, Ioffe, Vanhoucke, & Alemi, 2017), optical flow (Farnebäck, 2003), and shot distance analysis (Butler, 2012).…”

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confidence: 99%

Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture

Arnold¹,

Tilton²

2020

JOSS

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The Distant Viewing Toolkit is a Python package for the computational analysis of visual culture. It addresses the challenges of working with moving images through the automated extraction and visualization of metadata summarizing the content (e.g., people/actors, dialogue, scenes, objects) and style (e.g., shot angle, shot length, lighting, framing, sound) of time-based media. This toolkit is optimized for two purposes: (1) scholarly inquiry of visual culture from the humanities and social sciences, and (2) search and discovery of collections within libraries, archives, and museums.

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Color image (dis)similarity assessment and grouping based on dominant colors

Cited by 2 publications

References 7 publications

Image Search Using Similarity Measures Based on Circular Sectors

Image Search Using Similarity Measures Based on Circular Sectors

Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture

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