A Robust Salient Region Extraction Based on Color and Texture Features

In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents. However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.

Authors:

• Mingxin Zhang
• Zhaogan Lu
• Junyi Shen

Keywords:

• salient regions
• color and texture features
• image segmentation
• saliency metric

References:

[1] Hsieh Jun-Wei, Grimson W.E.L., Chiang Cheng-Chin,
Huang Yea-Shuan, "Region-based image retrieval", Proceedings of 2000
International Conference on Image Processing, Vol. 1, pp. 77-80, Sept.
2000.
[2] Feng Jing, Mingjing Li, Hong-Jiang Zhang, Bo Zhang, "An efficient and
effective region-based image retrieval framework", IEEE Transactions on
Image Processing, Vol. 13, No. 5, pp. 699-709, May 2004.
[3] Celebi E., Alpkocak A., "Semantic image retrieval and auto-annotation by
converting keyword space to image space", Proceedings of 12th
International Multi-Media Modelling Conference, pp. 153-160, Jan.
2006.
[4] Pappas T.N., Junqing Chen, Depalov D., "Perceptually based techniques
for image segmentation and semantic classification", IEEE
Communications Magazine, Vol. 45, No. 1, pp. 44-51, Jan. 2007.
[5] Jianping Fan, Yuli Gao, Hangzai Luo, Guangyou Xu, "Statistical
modeling and conceptualization of natural images", Pattern Recognition,
Vol. 38, No. 6, pp. 865-885, June 2005.
[6] Dadir T., Brady M., "Scale, saliency and image description", International
Journal of Computer Vision, Vol. 45, No. 2, pp. 83-105, 2001.
[7] Ling Shao, Michael Brady, "Invariant salient regions based image
retrieval under viewpoint and illumination variations", Journal of Visual
Communication and Image Representation, Vol. 17, No. 6, pp.
1256-1272, December 2006.
[8] KeDai Zhang, HanQing Lu, MiYi Duan, Qi Zhao, "Automatic Salient
Regions of Interest Extraction Based on Edge and Region Integration",
Proceedings of 2006 IEEE International Symposium on Industrial
Electronics, Vo1. 1, pp. 620-623, July 2006.
[9] Ling Shao, Timor Kadir and Michael Brady, "Geometric and photometric
invariant distinctive regions detection", Information Sciences, Vol. 177,
No. 4, pp. 1088-1122, February 2007.
[10] ByoungChul Ko, Soo Yeong Kwak, Hyeran Byun, "SVM-based salient
region(s) extraction method for image retrieval", Proceedings of the 17th
International Conference on Pattern Recognition, Vol. 2, pp. 977-980,
Aug. 2004.
[11] Yu-Hsin Kuan, Shih-Ting Chen, Chung Ming Kuo, Chaur-Heh Hsieh, "A
Novel Unsupervised Salient Region Segmentation for Color Images",
Proceedings of First International Conference on Innovative Computing,
Information and Control, Vol. 2, pp. 96-99, Aug. 2006.
[12] Kamarainen J.K., Kyrki V., Kalviainen H., "Invariance properties of
Gabor filter-based features-overview and applications", IEEE
Transactions on Image Processing, Vol. 15, No. 5, pp. 1088-1099, May
2006.
[13] Arivazhagan S., Ganesan L., Padam Priya S., "Texture classification
using Gabor wavelets based rotation invariant features", Pattern
Recognition Letters, Vol. 27, No. 16, pp. 1976-1982, December 2006.
[14] Comaniciu D., Meer P., "Mean shift: a robust approach toward feature
space analysis", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 5, pp. 603-619, May 2002.
[15] Mallat S., Hwang W.L., "Singularity detection and processing with
wavelet", IEEE Transactions on Information Theory, Vol. 38, No. 2, pp.
617-643, 1992.