Image Co-segmentation Using Maximum Common Subgraph Matching and Region Co-growing

Hati, Avik; Chaudhuri, Subhasis; Velmurugan, Rajbabu

doi:10.1007/978-3-319-46466-4_44

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…In addition to that, we have examined our unsupervised co-segmentation algorithm by using image pairs dataset, the barplot in Figure 9 shows the quantitative result of our algorithm comparing to the state-of-the-art methods [55][62] [63]. As shown here, our algorithm achieves the best F-measure comparing to all other state-of-the-art methods.…”

Section: Optimizationmentioning

confidence: 99%

“…Among the difficulties that make this problem a challenging one, we mention the similarity among the different backgrounds and the similarity of object and background [54] (see, e.g., the top row of Figure 6). A measure of "objectness" has proven to be effective in dealing with such problems and improving the co-segmentation results [54] [55]. However, this measure alone is not enough, especially when one aims to solve the problem using global pixel relations.…”

Section: Application To Co-segmentationmentioning

confidence: 99%

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Dominant Sets for “Constrained” Image Segmentation

Zemene

Alemu

Pelillo

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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Image segmentation has come a long way since the early days of computer vision, and still remains a challenging task. Modern variations of the classical (purely bottom-up) approach, involve, e.g., some form of user assistance (interactive segmentation) or ask for the simultaneous segmentation of two or more images (co-segmentation). At an abstract level, all these variants can be thought of as "constrained" versions of the original formulation, whereby the segmentation process is guided by some external source of information. In this paper, we propose a new approach to tackle this kind of problems in a unified way. Our work is based on some properties of a family of quadratic optimization problems related to dominant sets, a graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters that are constrained to contain predefined elements. In particular, we shall focus on interactive segmentation and co-segmentation (in both the unsupervised and the interactive versions). The proposed algorithm can deal naturally with several types of constraints and input modalities, including scribbles, sloppy contours and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions and constraints.

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Section: Optimizationmentioning

confidence: 99%

Section: Application To Co-segmentationmentioning

confidence: 99%

Dominant Sets for “Constrained” Image Segmentation

Zemene

Alemu

Pelillo

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…Method Faktor13 [7] Lee15 [27] Chang15 [1] Hati16 [14] Quan16 [46] Jerri.16 [21] Wang17 [57] Jerri.17 [20] Han18 [13] Hsu18 [17] Li19 [30] from 0.98 to 0.99. All in all, the superiority of the proposed method is verified through the comparison of the PR curves.…”

Section: Unsuperviesed Video Object Segmentation Taskmentioning

confidence: 99%

Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation

Zhen

Zhou

et al. 2020

Preprint

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In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn discriminative features (D-features) from the input images that reveal feature distribution from a global perspective. The Dfeatures are then used to establish correspondence with all features of test image under conditional random field (CRF) formulation, which is leveraged to enforce consistency between pixels. The experiments verify that DFNet outperforms state-of-the-art methods by a large margin with a mean IoU score of 83.4% and ranks first on the DAVIS-2016 leaderboard while using much fewer parameters and achieving much more efficient performance in the inference phase. We further evaluate DFNet on the FBMS dataset and the video saliency dataset ViSal, reaching a new state-of-the-art. To further demonstrate the generalizability of our framework, DFNet is also applied to the image object co-segmentation task. We perform experiments on a challenging dataset PASCAL-VOC and observe the superiority of DFNet. The thorough experiments verify that DFNet is able to capture and mine the underlying relations of images and discover the common foreground objects.

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“…Hati et al [59] formulated the image co-segmentation problem as a maximum common subgraph (MCS) computation problem. MCS was built on a region adjacency graph (RAG).…”

Section: ) Co-segmentation Techniques Based On Other Graphical Modelsmentioning

confidence: 99%

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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Image Co-segmentation Using Maximum Common Subgraph Matching and Region Co-growing

Cited by 10 publications

References 31 publications

Dominant Sets for “Constrained” Image Segmentation

Dominant Sets for “Constrained” Image Segmentation

Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation

A Review of Recent Advances in Image Co-Segmentation Techniques

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