Object Co-skeletonization with Co-segmentation

Jerripothula, Koteswar Rao; Cai, Jianfei; Lu, Jiangbo; Yuan, Junsong

doi:10.1109/cvpr.2017.413

Cited by 52 publications

(37 citation statements)

References 29 publications

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“…To further understand the image co-segmentation frameworks, we compared the co-segmentation performance of nine techniques: geometric mean saliency based cosegmentation (GMS) [121], group saliency propagation based co-segmentation (GSP) [122], saliency co-fusion based co-segmentation (SCF) [76], co-segmentation and cosketch (CSST) [89], co-segmentation and co-skeletonization (CSSL) [90], joint object discovery and segmentation (JODS) [21], decomposition multiple foreground co-segmentation (DMFC) [18], joint semantic matching and co-segmentation (JSMC) [100] and multiple random walkers based cosegmentation (MRW) [47] on iCoseg dataset, MSRC dataset, Internet dataset and Coseg-Rep dataset, respectively. The experimental results are produced by directly running the implementation codes from their websites.…”

Section: Resultsmentioning

confidence: 99%

“…Fig.7, Fig.8 and Fig.9 respectively show some visual cosegmentation results of GMS [121], GSP [122], SCF [76], CSST [89], CSSL [90] and MRW [47] on nine groups images, where Bear, Helicopter, Stongehenge from iCoseg dataset, Cat, Face, Plane from MSRC dataset, and Cranesbill, Fleabane, Seagull from Coseg-Rep dataset. Fig.10 shows some comparison co-segmentation results of JODS [21], DMFC [18], MRW [47], CSSL [90] and JSMC [100] on three groups images, where Airplane, Car, and Horse from Internet dataset. From Fig.7, Fig.8 and Fig.9, one can see that most of these techniques localize the foreground objects.…”

Section: Resultsmentioning

confidence: 99%

“…In [90], the shared objects from the input images have been detected using a coupling of two techniques, namely: co-segmentation and co-skeletonization. Co-skeletonization is defined as joint skeleton extraction of shared objects in the input images and skeleton provides good scribbles for segmentation.…”

Section: F Joint Optimization Based Frameworkmentioning

confidence: 99%

“…CO-SKEL [90] dataset is created by Jerripothula et al, which consists of 26 categories with 353 images of animals, birds, flowers and humans. These images are collected from the MSRC, Coseg-Rep, Weiaman Horses and iCoseg datasets along with their ground truth segmentation masks.…”

Section: A Datasets For Image Co-segmentationmentioning

confidence: 99%

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A Review of Recent Advances in Image Co-Segmentation Techniques

Lin

Wang

2019

IEEE Access

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Recent advances in the corporate processing of images have exhibited its advantages over the individual processing. Image co-segmentation aims to segment shared objects from two or more relevant images, and is becoming more and more interesting for computer vision researchers. Many applications need accurate and efficient segmentation techniques: indoor navigation, autonomous driving, and virtual reality systems to name a few. Although numerous techniques have been proposed, it is still lacking a deep review of image co-segmentation techniques. In this paper, we provide a review on the fundamentals and challenges of image co-segmentation techniques. We organize the recent advances of image co-segmentation into seven major frameworks: graph based framework, clustering based framework, partial differential equation based framework, quadratic programming based framework, low rank matrix recovery based framework, joint optimization based framework, and machine learning based framework. We expect this review to be beneficial to both fresh and senior researchers in this field. INDEX TERMS Image co-segmentation, deep learning, multiple foreground, single foreground.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: F Joint Optimization Based Frameworkmentioning

confidence: 99%

Section: A Datasets For Image Co-segmentationmentioning

confidence: 99%

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A Review of Recent Advances in Image Co-Segmentation Techniques

Lin

Wang

2019

IEEE Access

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“…A novel definition of an appearance medial axis transform (AMAT) has been proposed in [44], to detect symmetry in the wild in a purely bottom up, unsupervised fashion. In [15], the authors present an unconventional method based on joint co-skeletonization and co-segmentation.…”

Section: Related Workmentioning

confidence: 99%

DeepFlux for Skeletons in the Wild

Wang

Tsogkas

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recent methods frame object skeleton detection as a binary pixel classification problem, which is similar in spirit to learning-based edge detection, as well as to semantic segmentation methods. In the present article, we depart from this strategy by training a CNN to predict a twodimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms. This "image context flux" representation has two major advantages over previous approaches. First, it explicitly encodes the relative position of skeletal pixels to semantically meaningful entities, such as the image points in their spatial context, and hence also the implied object boundaries. Second, since the skeleton detection context is a region-based vector field, it is better able to cope with object parts of large width. We evaluate the proposed method on three benchmark datasets for skeleton detection and two for symmetry detection, achieving consistently superior performance over state-of-the-art methods.

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