Optimization of Salient Object Segmentation by using the influence of color in Digital Image

Human attention is more likely to be interested indifferent objects or striking in image processing called salientobject. Existing approaches worked well in finding the salientobject in this image, but they have not been able to accuratelydetect where objects should stand out due to the influence of lightintensity, there are various object results of salient object detectionin which area is still cut off or do not appear because they do notinclude salient area. We offer solutions to fix these problems byoptimizing salient object detection prioritizing object area aftersalient area, through checking comparison of the color regionlocated around the area of the salient. This Optimization of theapplication is able to improve to 83% from 100 salient object whichhas this problem, and able to produce more natural Saliency Cut

Saliency detection by adaptive clustering

Conference Name:2013 IEEE International Conference on Visual Communications and Image Processing, IEEE VCIP 2013. Conference Address: Kuching, Sarawak, Malaysia. Time:November 17, 2013 - November 20, 2013.IEEE; SARAWAK Convention Bureau; IEEE Circuits and Systems Society (CAS); neuraMATIX; Malaysia Convention and Exhibition BureauSaliency detection plays an important role in image segmentation, content-aware resizing and object recognition. Most approaches obtain promising performance recently, which is useful for the postprocessing. We propose a clustering-based method to detect refined regions with comparative performance. For coarse-grained classification with unknown clusters number, an adaptive algorithm called f-means is developed in this paper. Pixels are clustered by f-means based on color and spatial features, and then the centroids are used to compute their saliency values. Experiments show that our algorithm generates more fine maps, which outperform the state-of-the-art approaches on MSRA dataset. Relying on the saliency map, we also get superior results in foreground extracting, image resizing and thumbnails generation. ? 2013 IEEE