Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images

Tools

Yan, Yijun and Ren, Jinchang and Li, Yinsheng and Windmill, James F C and Ijomah, Winifred and Chao, Kuo Ming (2016) Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images. Multidimensional Systems and Signal Processing. pp. 1-24. ISSN 0923-6082 (https://doi.org/10.1007/s11045-016-0382-7)

[thumbnail of Fan-etal-OE-2015-Inferring-inherent-optical-properties-and-water-constituent-profiles]
Preview
 Text. Filename: Fan_etal_OE_2015_Inferring_inherent_optical_properties_and_water_constituent_profiles.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Due to their uniqueness and high value commercially, logos/trademarks play a key role in e-business based global marketing. However, existing trademark/logo retrieval techniques and content-based image retrieval methods are mostly designed for generic images, which cannot provide effective retrieval of trademarks/logos. Although color and spatial features have been intensively investigated for logo image retrieval, in most cases they were applied separately. When these are combined in a fused manner, a fixed weighting is normally used between them which cannot reflect the significance of these features in the images. When the image quality is degraded by various reasons such as noise, the reliability of color and spatial features may change in different ways, such that the weights between them should be adapted to such changes. In this paper, adaptive fusion of color and spatial descriptors is proposed for colored logo/trademark image retrieval. First, color quantization and k-means are combined for effective dominant color extraction. For each extracted dominant color, a component-based spatial descriptor is derived for local features. By analyzing the image histogram, an adaptive fusion of these two features is achieved for more effective logo abstraction and more accurate image retrieval. The proposed approach has been tested on a database containing over 2300 logo/trademark images. Experimental results have shown that the proposed methodology yields improved retrieval precision and outperforms three state-of-the-art techniques even with added Gaussian, salt and pepper, and speckle noise.

ORCID iDs

Yan, Yijun ORCID logoORCID: https://orcid.org/0000-0003-0224-0078, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Li, Yinsheng, Windmill, James F C ORCID logoORCID: https://orcid.org/0000-0003-4878-349X, Ijomah, Winifred and Chao, Kuo Ming;
CORE (COnnecting REpositories)