Interactive image segmentation by maximal similarity based region merging

Ning, Jifeng; Zhang, Lei; Wu, Chengke

doi:10.1016/j.patcog.2009.03.004

Cited by 371 publications

(232 citation statements)

References 32 publications

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“…a shape prior) into graph cut segmentation. Ning et al (2010) introduce a maximal similarity region merging based interactive image segmentation. Price et al (2010) combined geodesic distance information with edge information in a graph cut optimization framework.…”

Section: Related Workmentioning

confidence: 99%

SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

Cui¹,

Wang²,

Feng³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the kmeans cluster method, we get the bag of visual words, and then, we adopt three levels' spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

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Section: Related Workmentioning

confidence: 99%

SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

Cui¹,

Wang²,

Feng³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Before that, the feature definition of each superpixel is necessary. In [23], Color histogram was chosen as the superpixel feature. However, Color histogram only considers the color information each region but the frequency information.…”

Section: B Contourlet Transformmentioning

confidence: 99%

“…In order to guide the superpixel merging process, these superpixels must be represented by using some descriptor and define a rule for merging. In [23], the region was described in the color histogram which is an effective descriptor to r e p r e s e n t the object color feature. However, color histogram has its limitation because it only considers the color information of each superpixel but the frequency information.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…In addition, there are still many kinds of algorithm which are specific [14]- [22]. Recently, an interactive segmentation based on region merging (MSRM) is proposed by Ning [23]. The thought of this algorithm was to divide the original image into some regions by Mean-shift, and then merge the object regions and the background regions individually.…”

Section: Introductionmentioning

confidence: 99%

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Robust Interactive Segmentation Using Color Histogram and Contourlet Transform

Huang¹,

Pun²

2015

IJCTE

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 Abstract-Efficient and accurate image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is hard to handle with natural images and texture images with complex background, thus interactive scheme with a few simple user inputs is a very good addition to image segmentation. For the purpose to accurately extract objects from different images, this paper presents a color histogram and Contourlet transform based interactive image segmentation. In the initialization stage, a superpixel based initial segmentation is applied to the original image. After that, the original image will be divided into a certain number of superpixels. Then, each superpixel will be represented by a novel superpixel feature based on color histogram and Contourlet transform. Finally, by using user-defined strokes, we merge the superpixels which are similar to marked object superpixels and merge the superpixels similar to marked background superpixels, until classify all superpixels. The computational complexity is analyzed, and comparative experimental results show that the proposed scheme can reliably and rapidly extract the desired object from the complex background.

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“…The object will then extract from the background when merging process ends. Although the idea of region merging is first introduced by [23] this paper uses the region merging for obtaining the contour for object and then extracting desired object from image. The key contribution of the proposed method is a novel similarity based region merging technique, which is adaptive to image content and does not requires a present threshold.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

Saxena¹,

Jain²,

Singh³

2011

SpecialIssue

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Abstract-In this work; we address a novel interactive framework for object retrieval using unsupervised similar region merging and flood fill method which models the spatial and appearance relations among image pixels. Efficient and effective image segmentation is usually very hard for natural and complex images. This paper presents a new technique for similar region merging and objects retrieval. The users only need to roughly indicate the after which steps desired objects boundary is obtained during merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique. A region R is merged with its adjacent regions Q if Q has highest similarity with R among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 22 object classes (524 images total) show promising results.

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Interactive image segmentation by maximal similarity based region merging

Cited by 371 publications

References 32 publications

SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

Robust Interactive Segmentation Using Color Histogram and Contourlet Transform

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

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