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Large-Scale Object Mining for Object Discovery from Unlabeled Video [article]

Aljosa Osep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
2019 arXiv   pre-print
This paper addresses the problem of object discovery from unlabeled driving videos captured in a realistic automotive setting.  ...  In order to facilitate further research in object discovery, we release a collection of more than 360,000 automatically mined object tracks from 10+ hours of video data (560,000 frames).  ...  Acknowledgements: We would like to thank Bin Huang and Michael Krause for annotation work. This project was funded, in parts, by ERC Consolidator Grant DeeVise (ERC-2017-COG-773161).  ... 
arXiv:1903.00362v2 fatcat:mn7gephdsfcadfar7g4yzyrd4y

Towards Large-Scale Video Video Object Mining [article]

Aljosa Osep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
2018 arXiv   pre-print
We propose to leverage a generic object tracker in order to perform object mining in large-scale unlabeled videos, captured in a realistic automotive setting.  ...  We present a dataset of more than 360'000 automatically mined object tracks from 10+ hours of video data (560'000 frames) and propose a method for automated novel category discovery and detector learning  ...  In this paper, we propose a method for large-scale video-object mining.  ... 
arXiv:1809.07316v1 fatcat:mlm5oscinrfzlfqpihr6arpqhq

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video [article]

Aljoša Ošep and Paul Voigtlaender and Jonathon Luiten and Stefan Breuers and Bastian Leibe
2017 arXiv   pre-print
We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform.  ...  We propose a fully automatic approach for object mining from video which builds upon a generic object tracking approach.  ...  We would like to thank Alexander Hermans, Wolfgang Mehner and István Sárándi for helpful discussions  ... 
arXiv:1712.08832v1 fatcat:5uj26t7vijajrdcu3umhyexwha

Mining And-Or Graphs for Graph Matching and Object Discovery

Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
the AoG model from the unlabeled ARGs.  ...  This method provides a general solution to the problem of mining hierarchical models from unannotated visual data without exhaustive search of objects.  ...  The mined models can be used to 1) collect object samples from the unlabeled training data and 2) match objects in perviously unseen video frames/images.  ... 
doi:10.1109/iccv.2015.15 dblp:conf/iccv/ZhangWZ15 fatcat:57net2pgz5ahxoqipuf6j7i6au

Mining and cropping common objects from images

Gangqiang Zhao, Junsong Yuan
2010 Proceedings of the international conference on Multimedia - MM '10  
involved in exploring the huge solution space, including the location, scale, and the number of common objects.  ...  Our solution provides accurate localization of the common object, thus is able to crop the common objects despite their variations due to scale, view-point, lighting condition changes.  ...  Finally, it does not require a large number of images for data mining and works well to detect common object from a very limited number of images.  ... 
doi:10.1145/1873951.1874127 dblp:conf/mm/ZhaoY10 fatcat:dahaytnzena6tnnn3lb6hawnba

A Systematic Review on Existing Data Mining Approaches Envisioned for Knowledge Discovery from Multimedia

Benaka Santosha S, Chitra Kiran N
2018 International Journal of Electrical and Computer Engineering (IJECE)  
and discriminativefeatures associated with a video object.  ...  Hence, data mining has emerged as a field which having diverse aspects presently in extracting meaningful hidden patterns from complex image and video data considering different pattern classification  ...  Wang [44] have the framework of MapReduce is explored for large-scale multimedia data mining. Yang et al. [45] design follows a model-view-controller (MVC) pattern for applying semantics.  ... 
doi:10.11591/ijece.v8i2.pp908-916 fatcat:oot2s7xx55hcdoddjant7ny6xy

Unsupervised learning from video to detect foreground objects in single images [article]

Ioana Croitoru, Marius Leordeanu (1 and 2) University "Politehnica" of Bucharest)
2017 arXiv   pre-print
From a practical point of view, learning from unsupervised visual input has an immense practical value, as very large quantities of unlabeled videos can be collected at low cost.  ...  It learns to predict from a single input image (a video frame) the output for that particular frame, of a teacher pathway that performs unsupervised object discovery in video.  ...  Video discovery methods We also performed comparisons with methods specifically designed for object discovery in video.  ... 
arXiv:1703.10901v1 fatcat:mbpazfl5cfdqvh2uuy3oempmny

Modeling Scene and Object Contexts for Human Action Retrieval With Few Examples

Yu-Gang Jiang, Zhenguo Li, Shih-Fu Chang
2011 IEEE transactions on circuits and systems for video technology (Print)  
The use of context knowledge is critical for understanding human actions, which typically occur under particular scene settings with certain object interactions.  ...  Such a solution has tremendous potential in practice as it is often expensive to acquire large sets of training data.  ...  Incorporating Multiple Contextual Cues We now turn to the problem of how to use the learned coefficientsc for action discovery from new video data.  ... 
doi:10.1109/tcsvt.2011.2129870 fatcat:2ipnqennzneozoi2uhmd3lj3p4

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features [article]

Runsheng Zhang, Yaping Huang, Mengyang Pu, Jian Zhang, Qingji Guan, Qi Zou, Haibin Ling
2020 arXiv   pre-print
TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given.  ...  Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques  ...  CONCLUSION In this paper, we propose a novel pattern mining-based method, called Object Location Mining (OLM), for object discovery and localization from a single unlabeled image.  ... 
arXiv:1902.09968v3 fatcat:2col2budbjgzjoykscdl632qv4

Unsupervised learning of foreground object detection [article]

Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
2018 arXiv   pre-print
Our approach is different from published methods on unsupervised object discovery.  ...  In experiments our method achieves top results on three current datasets for object discovery in video, unsupervised image segmentation and saliency detection.  ...  object discovery in unlabeled videos, along the teacher pathway (module B in Figure 1 ).  ... 
arXiv:1808.04593v1 fatcat:n6invbkujzf37klilcbjt5o7fq

Unsupervised Learning from Videos for Object Discovery in Single Images

Dong Zhao, Baoqing Ding, Yulin Wu, Lei Chen, Hongchao Zhou
2020 Symmetry  
We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as  ...  This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network  ...  Object discovery from unlabeled data. Object discovery from unlabeled data is challenging due to the fact that it does not depend on any auxiliary information rather than a given unlabeled image.  ... 
doi:10.3390/sym13010038 fatcat:ohgmg6gmp5culcppemdmhvv42y

Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model

Qixiang Ye, Tianliang Zhang, Wei Ke, Qiang Qiu, Jie Chen, Guillermo Sapiro, Baochang Zhang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
The selflearning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation.  ...  Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based  ...  The resolution of the video is 704×576. 6000 frames were uniformly sampled from the long video for learning and 2600 frames for testing.  ... 
doi:10.1109/cvpr.2017.222 dblp:conf/cvpr/YeZKQCSZ17 fatcat:4b6uc7qvfvhkhj5gljgim5a7ai

Visual Graph Mining [article]

Quanshi Zhang, Xuan Song, Ryosuke Shibasaki
2017 arXiv   pre-print
Thus, from a practical perspective, such mining of maximal-size subgraphs can be regarded as a general platform for discovering and modeling the common objects within cluttered and unlabeled visual data  ...  theoretical basis of graph mining designed for tabular data.  ...  Visual mining: From the perspective of applications, there are numerous ways of mining objects from unlabeled big visual data, such as object discovery [35] , cosegmentation [17] , edge model extraction  ... 
arXiv:1708.03921v1 fatcat:jzhldngcl5gu5fgu5fjgvj42j4

Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints [chapter]

Dong Li, Wei-Chih Hung, Jia-Bin Huang, Shengjin Wang, Narendra Ahuja, Ming-Hsuan Yang
2016 Lecture Notes in Computer Science  
In this paper, we address the problem of unsupervised visual representation learning from a large, unlabeled collection of images.  ...  Specifically, we propose to use a cycle consistency criterion for mining positive pairs and geodesic distance in the graph for hard negative mining.  ...  Existing methods use various forms of clustering or matching algorithms for object discovery [23] , ROI detection [24] and patch mining [25, 26] .  ... 
doi:10.1007/978-3-319-46493-0_41 fatcat:g5pat4ss45a5liln65msaejcti

A Survey on Image Mining, its Techniques and Application

Vaibhavi S., Jay Vala
2016 International Journal of Computer Applications  
Image mining is challenging field which extends traditional data mining from structured data to unstructured data such as image data.  ...  We know that today's world is digital world and we have use digital data such as video, audio, images etc. in various fields for various purposes.  ...  An efficient as well as effective spatial clustering technique for large-scale multi-resolution incremental clustering which might be adaptable in vibrant environment; 2.  ... 
doi:10.5120/ijca2016907978 fatcat:bx374plfefg7bjfs5r24npvehu
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