Abstract
Many computer vision applications, such as object recognition and content-based image retrieval could function more reliably and effectively if regions of interest were isolated from their background. A new method for regions of interest extraction from color image based on visual saliency in HSV color space is proposed in this paper. Color saliency is calculated by a two-dimensional sigmoid function using the saturation component and brightness component, and we can identify regions with vivid color. Discrete Moment Transform (DMT)-based saliency can determine large areas of interest. A visual saliency map is obtained by combining color saliency and DMT-based saliency, which is denoted the S image. A criterion for the local homogeneity called the E image is calculated in the image. Based on S image and E image, the high visual saliency object seed points set and low visual saliency object seed points set are determined. The seeded regions growing and merging are used to extract regions of interest. Experimental results demonstrate the effectiveness and efficiency of the method for the natural color images.
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References
Wang J, Wiederhold G (2001) SIMPLIcity: semantics sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23:1–17
Huang C, Liu Q (2006) Color image retrieval using edge and edge-spatial features. Chin Opt Lett 4:457–459
Zhou Q, Ma L, Celenk M, Chelberg D (2005) Content-based image retrieval based on ROI detection and relevance feedback. Multimed Tools Appl 27:251–281
Treisman A (1985) Preattentive processing in vision. Comput Vis Graph Image Process 31:156–177
Aloimonos J, Weiss I, Bandyopadhyay I (1988) Active vision. Int J Comput Vis 1(4):333–236
Brown CM (1992) Issue in selected perception. Proc ICPR 1:21–30
Salembier P, Marques F (1999) Region-based representations of image and video: segmentation tools for multimedia services. IEEE Trans Circuits Syst Video Technol 9(8):1147–1169
Koch C, Ulman S (1985) Shifts in selection in visual attention: toward the underlying neural circuitry. Human Neurobiol 4:219–227
Itti L, Koch C, Niebwr E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259
Sun Y, Fisher R (2003) Object-based visual attention for computer vision. Artif Intell 146:77–123
Gesu VD, Valenti C, Strinati L (1997) Local operators to detect regions of interest. Pattern Recognit Lett 18:1077–1081
Pauwels EJ, Frederix G (1999) Finding salient regions in images. Comput Vis Image Underst 75(1/2):73–85
Lau HF, Levine MD (2002) Finding a small number of region in an image using low-level features. Pattern Recognit 35:2323–2339
Marchette DJ, Solka JL, Guidry R, Green J (1998) The advanced distributed region of interest tool. Pattern Recognit 31(12):2103–2118
Saad MA, Bovik AC (2009) Extracting regions of interest from still image: color saliency and wavelet-based approaches. In: Proc of IEEE 13th digital signal processing workshop and 5th IEEE signal processing education workshop, pp 540–543
Huang C, Liu Q, Yu S (2009) Automatic central object extraction from color image. In: Proc IEEE international conference on information engineer and computer science, vol 5, pp 3071–3074
Jing F, Li M, Zhang HJ, Zhang B (2003) Unsupervised image segmentation using local homogeneity analysis. In: Proc IEEE international symposium on circuits and systems, vol 2, pp 456–459
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Huang, C., Liu, Q. & Yu, S. Regions of interest extraction from color image based on visual saliency. J Supercomput 58, 20–33 (2011). https://doi.org/10.1007/s11227-010-0532-x
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DOI: https://doi.org/10.1007/s11227-010-0532-x