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Unsupervised co-segmentation through region matching

J. C. Rubio, J. Serrat, A. Lopez, N. Paragios
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene.  ...  Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background.  ...  In our third experiment we use the region matching output to identify parts of the common objects co-segmented.  ... 
doi:10.1109/cvpr.2012.6247745 dblp:conf/cvpr/RubioSLP12 fatcat:yza7midvtvbubcaq2bandxjaly

Image Co-skeletonization via Co-segmentation [article]

Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan
2020 arXiv   pre-print
Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: image co-skeletonization, which is defined as joint skeleton  ...  Therefore, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically.  ...  However, skeleton pixels usually lie in homogeneous regions (see Fig. 2 (d)&(e)) and are thus challenging to match.  ... 
arXiv:2004.05575v1 fatcat:ghzkddg5nrhi3fn2hkkbhyvt3e

Temporal Action Co-Segmentation in 3D Motion Capture Data and Videos

Konstantinos Papoutsakis, Costas Panagiotakis, Antonis A. Argyros
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Given two action sequences, we are interested in spotting/co-segmenting all pairs of sub-sequences that represent the same action. We propose a totally unsupervised solution to this problem.  ...  We treat this type of temporal action co-segmentation as a stochastic optimization problem that is solved by employing Particle Swarm Optimization (PSO).  ...  The method proposed in [40] performs unsupervised co-segmentation of multiple common regions of objects in multiple images.  ... 
doi:10.1109/cvpr.2017.231 dblp:conf/cvpr/PapoutsakisPA17 fatcat:zd6ycphggve4ja2ihkpyrbv3ja

From Logo to Object Segmentation

Fanman Meng, Hongliang Li, Guanghui Liu, King Ngi Ngan
2013 IEEE transactions on multimedia  
For new images, objects were segmented through searching the region that was most similar to the object template.  ...  [44] , an object co-segmentation method was proposed. The authors first segmented the images into a set of overlapping local regions.  ... 
doi:10.1109/tmm.2013.2280893 fatcat:txzytqmk2vgnzcs4k4qvl4yjja

Unsupervised Joint Object Discovery and Segmentation in Internet Images

Michael Rubinstein, Armand Joulin, Johannes Kopf, Ce Liu
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
Abstract We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections.  ...  a) Images downloaded from the Internet (b) Our automatic segmentation results (c) State-of-the-art co-segmentation results [8] Image search Object discovery Figure 1 .  ...  The notions of matching and saliency were also successfully applied by Fakor et al. [5] , a work done in parallel to ours, for unsupervised discovery of image categories. Co-segmentation.  ... 
doi:10.1109/cvpr.2013.253 dblp:conf/cvpr/RubinsteinJKL13 fatcat:qx2sniq3ubaabeg7sk6rb53hqm

Data-Driven Shape Analysis and Processing

Kai Xu, Vladimir G. Kim, Qixing Huang, Evangelos Kalogerakis
2016 Computer graphics forum (Print)  
Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction  ...  Unsupervised segmentation Unsupervised data-driven shape segmentation techniques fall into two categories: clustering-based techniques and matching-based techniques.  ...  Tens Local No Joint part matching Unsupervised Segment similarity Seg./Corr. [HWG14] Mesh Init. corr. Tens Global No Consistent func. map networks Unsupervised Segment similarity Seg.  ... 
doi:10.1111/cgf.12790 fatcat:q76sq2syjvce5fjjjb7yoafww4

Effective Image Co-Segmentation using Modified Higher Order Algorithm

2019 International journal of recent technology and engineering  
Both the quantitative or qualitative pilot results over consultant datasets exhibit that the rigor on our co-segmentation consequences is lots higher than the cutting-edge co-segmentation methods.  ...  Then, a current co-segmentation electricity characteristic the usage of higher kilter cliques is developed, who may efficaciously co-segment the foreground objects with vast appearance editions beyond  ...  according contributions. 1) We formulate the interactive image co-segmentation through gamble determination yet excessive-order energy optimization, who usage the region likelihoods on a pair over pictures  ... 
doi:10.35940/ijrte.c1062.1083s219 fatcat:o7uyr3uaefh5roc2g5bw7p664u

Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization [article]

Luke Melas-Kyriazi and Christian Rupprecht and Iro Laina and Andrea Vedaldi
2022 arXiv   pre-print
Furthermore, by clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions, i.e. semantic segmentations.  ...  Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled data.  ...  Method mIoU Pretext task methods Co-Occurrence Semantic Segmentation We now consider the challenging setting of unsupervised semantic segmentation, which differs from object segmentation in that it  ... 
arXiv:2205.07839v1 fatcat:6iyz22evgzak5h2apmxbywowuq

Unsupervised detection and segmentation of identical objects

Minsu Cho, Young Min Shin, Kyoung Mu Lee
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
We address an unsupervised object detection and segmentation problem that goes beyond the conventional assumptions of one-to-one object correspondences or modeltest settings between images.  ...  To detect and segment all the object-level correspondences from the given images, a novel multi-layer match-growing method is proposed that starts from initial local feature matches and explores the images  ...  Co-recognition [3] and quasi-dense matching [9] recognize and segment common object pairs directly from image pairs containing common objects with clutter and occlusion.  ... 
doi:10.1109/cvpr.2010.5539777 dblp:conf/cvpr/ChoSL10 fatcat:vlij327oorfddogpe7acsasjmu

Unsupervised Building Extraction from Multimodal Aerial Data Based on Accurate Vegetation Removal and Image Feature Consistency Constraint

Yan Meng, Shanxiong Chen, Yuxuan Liu, Li Li, Zemin Zhang, Tao Ke, Xiangyun Hu
2022 Remote Sensing  
These comparative results verify that our unsupervised methods combining multisource data are very effective.  ...  For multimodal data with sufficient information, extracting buildings accurately in as unsupervised a manner as possible.  ...  We also adopt the strategy of using image-derived boundaries to replace LiDARderived boundaries, but through region matching instead of boundary matching.  ... 
doi:10.3390/rs14081912 fatcat:6c4w5x7l3nemfn6vnl6fvby6ue

Adaptive segmentation of plant images, an integration of color space features and self-organizing maps

Mahmood Golzarian
2011 2011 International Conference on Multimedia Technology  
We developed an adaptive learning for segmentation of plant images into plant and non-plant regions.  ...  However, for images with higher complexity where there are more regions with similar color pattern, the method produces some noise.  ...  In this study, we developed an unsupervised SOM for the segmentation of the color images of narrow leaf plants.  ... 
doi:10.1109/icmt.2011.6001833 fatcat:npoqqs3fn5aehd2chukyjatdga

MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation [article]

Jiaxing Huang, Dayan Guan, Shijian Lu, Aoran Xiao
2022 arXiv   pre-print
Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation.  ...  MLAN has two novel designs, namely, region-level adversarial learning (RL-AL) and co-regularized adversarial learning (CR-AL).  ...  Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through  ... 
arXiv:2103.12991v2 fatcat:ordpzyl5gnepfhyifjgbrhfk3q

Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers [article]

Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, Stella X. Yu
2022 arXiv   pre-print
Capturing visual similarity and statistical co-occurrences, HSG also outperforms existing unsupervised segmentation methods by a large margin on five major object- and scene-centric benchmarks.  ...  We approach unsupervised segmentation as a pixel-wise feature learning problem.  ...  The evaluation metric is based on F-score of region matching. We next demonstrate the efficacy of our hierarchical clustering transformer.  ... 
arXiv:2204.11432v1 fatcat:kqhf6u7w4jgnnicm2z2nvwzezu

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering [chapter]

Shereen Fouad, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini
2017 Communications in Computer and Information Science  
This work was supported by the EPSRC through funding under grant EP/M023869/1 "Novel context-based segmentation algorithms for intelligent microscopy".  ...  The method is easy to understand and implement and specially tailored for unsupervised imaging segmentations.  ...  SLIC superpixels have been used before to facilitate and improve unsupervised segmentation of histopathological images.  ... 
doi:10.1007/978-3-319-60964-5_67 fatcat:6owplf7q7vh4zikjtw5ohapy3u


K. Karantzalos, P. Koutsourakis, I. Kalisperakis, L. Grammatikopoulos
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map.  ...  In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised  ...  Terrain classes are detected through an unsupervised multi-region segmentation and rule-based classification process.  ... 
doi:10.5194/isprsarchives-xl-1-w4-293-2015 fatcat:ifkbhuqldrdgdjsp5uj75y4nvm
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