Natural Image Segmentation with Adaptive Texture and Boundary Encoding

Rao, Shankar; Mobahi, Hossein; Yang, Allen Y.; Sastry, S. Shankar; Ma, Yi

doi:10.1007/978-3-642-12307-8_13

Cited by 87 publications

(79 citation statements)

References 23 publications

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“…Most automatic image segmentation algorithms ignore figure/ground organization, producing a two-dimensional partition of the image with no notion of figure or depth ordering [28,6,30,8,27,1,23,3]. Other work treats depth recovery itself as an end goal, exploiting segmentation along with scene geometry (e.g.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding

Maire

2010

Computer Vision – ECCV 2010

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Abstract. Image segmentation and figure/ground organization are fundamental steps in visual perception. This paper introduces an algorithm that couples these tasks together in a single grouping framework driven by low-level image cues. By encoding both affinity and ordering preferences in a common representation and solving an Angular Embedding problem, we allow segmentation cues to influence figure/ground assignment and figure/ground cues to influence segmentation. Results are comparable to state-of-the-art automatic image segmentation systems, while additionally providing a global figure/ground ordering on regions.

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Section: Introductionmentioning

confidence: 99%

Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding

Maire

2010

Computer Vision – ECCV 2010

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“…The main idea behind compression based methods [26] is that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data [75] [76]. The concepts of the optimal segmentation and the coding length are inter-related in the way that segmentation is done in a manner so that the regularities of the pattern, if occurring in an image, can be successfully compressed.…”

Section: Compression Based Methodsmentioning

confidence: 99%

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Agarwal¹,

Singh²

2013

IJCA

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This literature review attempts to provide a brief overview of some of the most common image segmentation techniques. It discusses Edge detection technique, Thresholding technique, Region growing based technique, Watershed technique, Compression based method, Histogram based segmentation and Graph partitioning method. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper different method of implementing genetic algorithm has been reviewed. Finally, summaries and review of research work on wrapper approach for image segmentation techniques has been represented.

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“…We compare the performance of our method to several publicly available image segmentation methods, which we refer to as NC [12], MS [13], gpbowt-UCM [25], F-H [47], NTP [48], Saliency [49], TBES [50], and LAS [21]. The reported results of these methods are cited from the above papers, respectively.…”

Section: Berkeley Segmentation Datasetmentioning

confidence: 99%

Affinity Learning with Diffusion on Tensor Product Graph

Yang

Prasad

Latecki

2013

IEEE Trans. Pattern Anal. Mach. Intell.

145

127

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Abstract-In many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches, more reliable similarities can be obtained if the original similarities are diffused in the context of other data points, where the context of each point is a set of points most similar to it. Compared to the existing methods, our approach differs in two main aspects. First, instead of diffusing the similarity information on the original graph, we propose to utilize the tensor product graph (TPG) obtained by the tensor product of the original graph with itself. Since TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities. However, it comes at the price of higher order computational complexity and storage requirement. The key contribution of the proposed approach is that the information propagation on TPG can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph. We prove that a graph diffusion process on TPG is equivalent to a novel iterative algorithm on the original graph, which is guaranteed to converge. After its convergence we obtain new edge weights that can be interpreted as new, learned affinities. We stress that the affinities are learned in an unsupervised setting. We illustrate the benefits of the proposed approach for data manifolds composed of shapes, images, and image patches on two very different tasks of image retrieval and image segmentation. With learned affinities, we achieve the bull's eye retrieval score of 99.99 percent on the MPEG-7 shape dataset, which is much higher than the state-of-the-art algorithms. When the data points are image patches, the NCut with the learned affinities not only significantly outperforms the NCut with the original affinities, but it also outperforms state-of-the-art image segmentation methods.

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Natural Image Segmentation with Adaptive Texture and Boundary Encoding

Cited by 87 publications

References 23 publications

Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding

Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Affinity Learning with Diffusion on Tensor Product Graph

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