Abstract
Saliency in a scene describes those facets of any stimulus that makes it stand out from the masses. Saliency detection has attracted numerous algorithms in recent past and proved to be an important aspect in object recognition, image compression, classification and retrieval tasks. The present method makes two complementary saliency maps namely color and texture. The method employs superpixel segmentation using Simple Linear Iterative Clustering (SLIC). The tiny regions obtained are further clustered on the basis of homogeneity using DBSCAN. The method also employs two levels of quantization of color that makes the saliency computation easier. Basically, it is an adaptation to the property of the human visual system by which it discards the less frequent colors in detecting the salient objects. Furthermore, color saliency map is computed using the center surround principle. For texture saliency map, Gabor filter is employed as it is proved to be one of the appropriate mechanisms for texture characterization. Finally, the color and texture saliency maps are combined in a non-linear manner to obtain the final saliency map. The experimental results along with the performance measures have established the efficacy of the proposed method.
Similar content being viewed by others
References
Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. In: International conference on computer vision systems. Springer, pp 66–75
Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE conference on computer vision and pattern recognition, 2009. cvpr 2009, IEEE, pp 1597–1604
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282
Chang KY, Liu T L, Chen H T, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 914–921
Chang KY, Liu TL, Lai SH (2011) From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: 2011 IEEE conference on computer vision and pattern recognition (cvpr), IEEE, pp 2129–2136
Chen T, Cheng MM, Tan P, Shamir A, Hu SM (2009) Sketch2photo: Internet image montage. ACM Trans Graph (TOG) 28(5):124
Chen Zh, Liu Y, Sheng B, Jn Liang, Zhang J, Yb Yuan (2016) Image saliency detection using gabor texture cues. Multimed Tools Appl 75(24):16,943–16,958
Cheng MM, Mitra NJ, Huang X, Torr PH, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582
Clausi DA, Jernigan ME (2000) Designing gabor filters for optimal texture separability. Pattern Recog 33(11):1835–1849
Goferman S, Zelnik-Manor L, Tal A (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926
Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198
Han J, Ngan KN, Li M, Zhang HJ (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circuits Syst Video Technol 16 (1):141–145
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in neural information processing systems, pp 545–552
Hiremath P, Pujari J (2008) Content based image retrieval using color boosted salient points and shape features of an image. Int J Image Process 2(1):10–17
Hou X, Zhang L (2007 ) Saliency detection: A spectral residual approach. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8
Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194–201
Hu Y, Rajan D, Chia LT (2005) Robust subspace analysis for detecting visual attention regions in images. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 716–724
Huang H, Zhang L, Fu TN (2010) Video painting via motion layer manipulation. In: Computer graphics forum, vol 29. Wiley Online Library, pp 2055–2064
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259
Ji QG, Fang ZD, Xie ZH, Lu ZM (2013) Video abstraction based on the visual attention model and online clustering. Signal Process Image Commun 28 (3):241–253
Jiang P, Ling H, Yu J, Peng J (2013) Salient region detection by ufo: Uniqueness, focusness and objectness. In: 2013 IEEE international conference on computer vision (ICCV). IEEE, pp 1976–1983
Jonathan H, Christof K, Pietro P (2006) Graph-based visual saliency. In: Proceedings of the 20th annual conference on neural information processing systems, pp 545–552
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2106–2113
Klein D A, Frintrop S (2011) Center-surround divergence of feature statistics for salient object detection. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 2214–2219
Kurita T, Otsu N, Abdelmalek N (1992) Maximum likelihood thresholding based on population mixture models. Pattern Recogn 25(10):1231–1240
Li A, She X, Sun Q (2013) Color image quality assessment combining saliency and fsim. In: 5th international conference on digital image processing (ICDIP 2013), international society for optics and photonics, vol 8878, p 88780I
Li J, Levine MD, An X, Xu X, He H (2013) Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans Pattern Anal Mach Intell 35(4):996–1010
Li X, Li Y, Shen C, Dick A, Van Den Hengel A (2013) Contextual hypergraph modeling for salient object detection. In: 2013 IEEE international conference on computer vision (ICCV). IEEE, pp 3328– 3335
Liu F, Gleicher M (2006) Region enhanced scale-invariant saliency detection. In: 2006 IEEE international conference on multimedia and expo. IEEE, pp 1477–1480
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum HY (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367
Lou J, Ren M, Wang H (2014) Regional principal color based saliency detection. PloS one 9(11):e112,475
Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the 11th ACM international conference on multimedia. ACM, pp 374–381
Meger D, Forssen PE, Lai K, Helmer S, McCann S, Southey T, Baumann M, Little JJ, Lowe DG (2008) Curious george: an attentive semantic robot. Robot Auton Syst 56(6):503–511
Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Perazzi F, Krahenbuhl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 733–740
Portilla J, Navarro R, Nestares O, Tabernero A (1996) Texture synthesis-by-analysis method based on a multiscale early-vision model. Opt Eng 35(8):2403–2417
Ren YF, Mu ZC (2014) Salient object detection based on global contrast on texture and color. In: 2014 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 7–12
Rosin PL (2009) A simple method for detecting salient regions. Pattern Recogn 42(11):2363–2371
Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004, CVPR 2004, vol 2. IEEE, pp II–II
Sharma G, Jurie F, Schmid C (2012) Discriminative spatial saliency for image classification. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3506–3513
Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 853–860
Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312
Tatler BW (2007) The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. J Vis 7(14):4–4
Tian H, Fang Y, Zhao Y, Lin W, Ni R, Zhu Z (2014) Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans Image Process 23(10):4389–4398
Valenti R, Sebe N, Gevers T (2009) Image saliency by isocentric curvedness and color. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2185–2192
Wang P, Wang J, Zeng G, Feng J, Zha H, Li S (2012) Salient object detection for searched web images via global saliency. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3194–3201
Xia C, Qi F, Shi G, Wang P (2015) Nonlocal center–surround reconstruction-based bottom-up saliency estimation. Pattern Recog 48(4):1337–1348
Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1155–1162
Yu Z, Wong H S (2007) A rule based technique for extraction of visual attention regions based on real-time clustering. IEEE Trans Multimed 9(4):766–784
Zhai Y, Shah M (2006) Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th ACM international conference on multimedia. ACM, pp 815–824
Zhang L, Yang L, Luo T (2016) Unified saliency detection model using color and texture features. PloS One 11(2):e0149,328
Zhou L, Yang Z, Yuan Q, Zhou Z, Hu D (2015) Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans Image Process 24(11):3308–3320
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rafi, M., Mukhopadhyay, S. Salient object detection employing regional principal color and texture cues. Multimed Tools Appl 78, 19735–19751 (2019). https://doi.org/10.1007/s11042-019-7153-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7153-z