Object Color Estimation with Dominant Color in Automated Image Semantic Description Generation Tasks

Authors

  • Aghasi S. Poghosyan Institute for Informatics and Automation Problems of NAS RA

DOI:

https://doi.org/10.51408/1963-0036

Keywords:

Dominant color, Image color palette, Color name selection, Object color detection, , Image semantic analysis

Abstract

The automated image tagging is an important part of modern search engines. The generated image tags can be constructed from object names and their attributes, for example, colors. This work presents an object color name detection real-time algorithm. It is applicable to any automatic object detection and localization systems. The presented algorithm is fast enough to run after the existing real-time object detection system, without adding visible overhead. The algorithm uses k-means to detect the dominant color and selects the correct name for the color via Delta E (CIE 2000).

References

S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems 28, 2015.

J. Dai, Y. Li, K. He and J. Sun, “R-fcn: Object detection via region-based fully convolutional networks,” in Advances in neural information processing systems 29, 2016.

C. Szegedy, S. Reed, D. Erhan, D. Anguelov and S. Ioffe, “Scalable, high-quality object detection,” arXiv preprint arXiv:1412.1441, 2014.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, “Ssd: Single shot multibox detector,” in European Conference on Computer Vision, pp. 21-37, 2016.

J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified, realtime object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.

O. Vinyals, A. Toshev, S. Bengio and D. Erhan, "Show and tell: A neural image caption generator," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156-3164, 2015.

A. Poghosyan and H. Sarukhanyan, “Short-term memory with read-only unit in neural image caption generator,” 11-th International Conference Computer Science andInformation Technologies, Revised Selected Papers, IEEE Xplore, DOI: 10.1109/CSITechnol.2017.8312163, pp. 162-167, 2017.

A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128—3137, 2015.

A. Poghosyan and H. Sarukhanyan, “Image caption generation model based on object detector”, Transactions of IIAP NAS RA, Mathematical Problems of Computer Science, vol. 50, pp. 5-14, 2018.

K. Duan, D. Parikh, D. Crandall and K. Grauman, “Discovering localized attributes for fine-grained recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1346—1353, 2012.

J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A k-means clustering algorithm” Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, pp. 100- 108, 1979.

G. Sharma, W. Wu and E. N. Dalal, “The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations,” Color Research & Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur, vol. 30, pp. 21-30, 2005.

Downloads

Published

2021-12-10

How to Cite

Poghosyan, A. S. (2021). Object Color Estimation with Dominant Color in Automated Image Semantic Description Generation Tasks. Mathematical Problems of Computer Science, 51, 90–97. https://doi.org/10.51408/1963-0036