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Large-scale Image Retrieval using Neural Net Descriptors

David Novak, Michal Batko, Pavel Zezula
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
in real time and on a large scale.  ...  Keywords metric indexing; deep convolutional neural network; contentbased image retrieval; k-NN search ONLINE IMAGE RETRIEVAL SYSTEM One of current big challenges in computer science is development of  ...  in real time and on a large scale.  ... 
doi:10.1145/2766462.2767868 dblp:conf/sigir/NovakBZ15 fatcat:ehofgbfl5ra6jagjbe52ytyojm

From Coarse to Fine: Robust Hierarchical Localization at Large Scale

Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Marcin Dymczyk
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale  ...  It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes.  ...  Timings of NV+SP and HF-Net show that our coarse-to-fine approach scales well to large environments. The global search is fast, and only depends on the number of images used to build the model.  ... 
doi:10.1109/cvpr.2019.01300 dblp:conf/cvpr/SarlinCSD19 fatcat:kiaadibacne7tmiyfgsmtqhnt4

Neural Codes for Image Retrieval [chapter]

Artem Babenko, Anton Slesarev, Alexandr Chigorin, Victor Lempitsky
2014 Lecture Notes in Computer Science  
In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application.  ...  It has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image.  ...  This makes the use of neural codes particularly attractive for large-scale retrieval applications, where the memory footprint of a descriptor often represents the major bottleneck.  ... 
doi:10.1007/978-3-319-10590-1_38 fatcat:s3ad4dk34jgdbawkkorr7wseba

Neural Codes for Image Retrieval [article]

Artem Babenko, Anton Slesarev, Alexandr Chigorin, Victor Lempitsky
2014 arXiv   pre-print
In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application.  ...  task (e.g.\ Image-Net).  ...  This makes the use of neural codes particularly attractive for large-scale retrieval applications, where the memory footprint of a descriptor often represents the major bottleneck.  ... 
arXiv:1404.1777v2 fatcat:rzhl4fk5pbfljjlslxvkrbfn4q

From Coarse to Fine: Robust Hierarchical Localization at Large Scale [article]

Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Marcin Dymczyk
2019 arXiv   pre-print
By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale  ...  It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes.  ...  A version of our method is based on existing neural networks for image retrieval and feature matching.  ... 
arXiv:1812.03506v2 fatcat:7ccgbre2ezcn3oj2526jxbuvfy

Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks [article]

Zhiyong Dou, Haotian Cui, Lin Zhang, Bo Wang
2020 arXiv   pre-print
Experiments on several large benchmark datasets demonstrate effectiveness of our method over state-of-the-art diffusion algorithms for unsupervised image retrieval.  ...  Diffusion has shown great success in improving accuracy of unsupervised image retrieval systems by utilizing high-order structures of image manifold.  ...  Fig. 6 plots the t-SNE visualizations of the Oxford5k images using the original image descriptors and the learned features of GRAD-Net respectively.  ... 
arXiv:2001.01284v2 fatcat:4df6vmsrxzhehgppiu6637baua

Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks [article]

Alexandr Notchenko, Ermek Kapushev, Evgeny Burnaev
2017 arXiv   pre-print
In this paper we present results of performance evaluation of S3DCNN - a Sparse 3D Convolutional Neural Network - on a large-scale 3D Shape benchmark ModelNet40, and measure how it is impacted by voxel  ...  We demonstrate comparable classification and retrieval performance to state-of-the-art models, but with much less computational costs in training and inference phases.  ...  Big Thanks to Benjamin Graham for some useful comments and ideas. Thanks to Rasim Akhunzyanov for his help in debugging the PySparseConvNet code.  ... 
arXiv:1611.09159v2 fatcat:2px6e4vgzjggtgcto4hpuo4zy4

Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition [article]

Le Hui, Mingmei Cheng, Jin Xie, Jian Yang
2021 arXiv   pre-print
By distilling the knowledge from EPC-Net, EPC-Net-L can obtain discriminative global descriptors for retrieval.  ...  Existing deep learning based global descriptors for the retrieval task usually consume a large amount of computation resources (e.g., memory), which may not be suitable for the cases of limited hardware  ...  Large-Scale Place Recognition Many efforts [20] , [22] , [23] , [41] - [45] have been introduced for point cloud based large-scale place recognition.  ... 
arXiv:2101.02374v1 fatcat:zm2mkiyp4vgojbt32ebpu5e3ji

AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality

Weiquan Liu, Cheng Wang, Xuesheng Bian, Shuting Chen, Wei Li, Xiuhong Lin, Yongchuan Li, Dongdong Weng, Shang-Hong Lai, Jonathan Li
2019 Remote Sensing  
with the handcrafted descriptors or existing feature learning neural networks very challenging.  ...  Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered  ...  Acknowledgments: We thank the reviewers for their careful reading and valuable comments, which helped us to improve the manuscript.  ... 
doi:10.3390/rs11192243 fatcat:r7seh3al45g7hjaw6f7kx3z5sy

Perlustration on Image Processing under Free Hand Sketch Based Image Retrieval

S. Amarnadh, P.V.G.D. Reddy, N.V.E.S. Murthy
2018 EAI Endorsed Transactions on Internet of Things  
Image Retrieval to provide the results in a better way by adapting the approaches like Text Based Image Retrieval(TBIR) and Sketch Based Image Retrieval(SBIR).  ...  The idea of this paper is to present the survey on sketch based image retrieval adapting deep learning concept on the mobile platforms, by presenting various methodologies and techniques.  ...  [4] presented a work on large scale sketch-based image retrieval, for searching a image over million images. They used descriptors to preprocess the sketches and images.  ... 
doi:10.4108/eai.21-12-2018.159334 fatcat:2wjongwrhrfflm2amb3zyd52b4

No Fear of the Dark: Image Retrieval under Varying Illumination Conditions [article]

Tomas Jenicek, Ondřej Chum
2019 arXiv   pre-print
Prior to extracting image descriptors by a convolutional neural network, images are photometrically normalised in order to reduce the descriptor sensitivity to illumination changes.  ...  Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned.  ...  When using a learned normalisation, the query image is first normalised (bottom left) and then used to retrieve images using the same procedure (bottom row). lections, for example change of scale, such  ... 
arXiv:1908.08999v1 fatcat:2yrf6v4nw5evnb3urqi7yrbw5m

Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing

Jie Lin, Olivier Morere, Julie Petta, Vijay Chandrasekhar, Antoine Veillard
2016 2016 Data Compression Conference (DCC)  
Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand  ...  With internet-scale image databases, like the recently released Yahoo 100M image database [11] , compact global descriptors will be key to fast web-scale image-retrieval.  ...  Holidays+1M: Recall @ R = 1000 (b) UKbench+1M: Recall @ R = 1000 Figure 4 . 4 Large scale retrieval results (with 1 million distractor images) for different compression schemes.  ... 
doi:10.1109/dcc.2016.23 dblp:conf/dcc/LinMPCV16 fatcat:qxdp7hxcpbe6ffk3ck5jta45pu

Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers [article]

Christof Henkel
2021 arXiv   pre-print
We show how to combine and enhance concepts from recent research in image retrieval and introduce two architectures especially suited for large-scale landmark identification.  ...  Furthermore, we elaborate a novel discriminative re-ranking methodology for image retrieval.  ...  Conclusion We presented several improvements to previous approaches for large-scale landmark identification leading to winning both tracks of the 2021 Google landmark competition.  ... 
arXiv:2110.03786v2 fatcat:rzgx7twnifftrabmq6nkuk6uya

Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing [article]

Jie Lin, Olivier Morère, Julie Petta, Vijay Chandrasekhar, Antoine Veillard
2015 arXiv   pre-print
Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand  ...  A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches.  ...  Fig. 4 . 4 Large scale retrieval results (with 1 million distractor images) for different compression schemes.  ... 
arXiv:1511.03055v1 fatcat:owk7tvr3ibectc6f5u2knokggi

Group Invariant Deep Representations for Image Instance Retrieval [article]

Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso Poggio
2016 arXiv   pre-print
Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global  ...  descriptors for image instance retrieval.  ...  from CNNs for large scale image-retrieval.  ... 
arXiv:1601.02093v2 fatcat:x4k5sntopzg27mvw2sssy36ydm
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