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Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval [article]

Joel Brogan, Aparna Bharati, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer
2019 arXiv   pre-print
We propose a new approach for spatial verification that aims at modeling object-level regions dynamically clustering keypoints in a 2D Hough space, which are then used to accurately weight small contributing  ...  We call this method Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations on CPUs.  ...  Our method belongs to the latter category of spatial verification techniques and is agnostic to the chosen local features and feature indexing approach.  ... 
arXiv:1903.10019v4 fatcat:ndke65wvkrftjn5wlneb3ea3um

Local Features and Visual Words Emerge in Activations [article]

Oriane Siméoni, Yannis Avrithis, Ondrej Chum
2019 arXiv   pre-print
We propose a novel method of deep spatial matching (DSM) for image retrieval.  ...  No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process.  ...  This is used for spatial verification in large-scale image retrieval. Inlier correspondences shown, colored by visual word. What is the underlying representation?  ... 
arXiv:1905.06358v1 fatcat:77vxuxpmazh3ddjipg3brtoiei

Local Features and Visual Words Emerge in Activations

Oriane Simeoni, Yannis Avrithis, Ondrej Chum
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We propose a novel method of deep spatial matching (DSM) for image retrieval.  ...  No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process.  ...  This is used for spatial verification in large-scale image retrieval. Inlier correspondences shown, colored by visual word. What is the underlying representation?  ... 
doi:10.1109/cvpr.2019.01192 dblp:conf/cvpr/SimeoniAC19 fatcat:r2kl56tjzfhtfnisqx2f37dxna

Efficient Visual Recognition

Li Liu, Matti Pietikäinen, Jie Qin, Wanli Ouyang, Luc Van Gool
2020 International Journal of Computer Vision  
binary codes for personal- ized image retrieval Personalized image retrieval A general framework for deep supervised discrete hashing Image retrieval Product quantization network for fast visual  ...  consistent large margin proxy embeddings Image retrieval Unified binary generative adversarial network for image retrieval and compression Image retrieval, image compression Learning multifunctional  ... 
doi:10.1007/s11263-020-01351-w fatcat:mbcq6shmerbo5njayscgb3t4rq

Deep Image Retrieval: Learning global representations for image search [article]

Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus
2016 arXiv   pre-print
It even surpasses most prior works based on costly local descriptor indexing and spatial verification. Additional material is available at www.xrce.xerox.com/Deep-Image-Retrieval.  ...  We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors.  ...  Leveraging large-scale noisy data To train our network for instance-level image retrieval we leverage a large-scale image dataset, the Landmarks dataset [17] , that contains approximately 214K images  ... 
arXiv:1604.01325v2 fatcat:neulmvop7beinpegiirexv3zcq

Faster R-CNN Features for Instance Search [article]

Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques, Shin'ichi Satoh
2016 arXiv   pre-print
We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve.  ...  This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN.  ...  The Image Processing Group at the UPC is a SGR14 Consolidated Research Group recognized and sponsored by the Catalan Government (Generalitat de Catalunya) through its AGAUR office.  ... 
arXiv:1604.08893v1 fatcat:wasciox3bbeaddii6d7jmszpia

Detect-to-Retrieve: Efficient Regional Aggregation for Image Search [article]

Marvin Teichmann, Andre Araujo, Menglong Zhu, Jack Sim
2019 arXiv   pre-print
However, due to the lack of bounding-box datasets for objects of interest among retrieval benchmarks, most recent work on regional representations has focused on either uniform or class-agnostic region  ...  R-ASMK boosts image retrieval accuracy substantially with no dimensionality increase, while even outperforming systems that index image regions independently.  ...  a large-scale image retrieval benchmark [28] .  ... 
arXiv:1812.01584v2 fatcat:dicbws7pdfaoxbd4pezucmmv7q

Faster R-CNN Features for Instance Search

Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques, Shin'ichi Satoh
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve.  ...  This work explores the suitability for instance retrieval of image-and region-wise representations pooled from an object detection CNN such as Faster R-CNN.  ...  The Image Processing Group at the UPC is a SGR14 Consolidated Research Group recognized and sponsored by the Catalan Government (Generalitat de Catalunya) through its AGAUR office.  ... 
doi:10.1109/cvprw.2016.56 dblp:conf/cvpr/SalvadorNMS16 fatcat:2xzm6ebefffrhnopdai2aeeirm

A Comparison of Dense Region Detectors for Image Search and Fine-Grained Classification

Ahmet Iscen, Giorgos Tolias, Philippe-Henri Gosselin, Herve Jegou
2015 IEEE Transactions on Image Processing  
In this context, the most common approach is to extract the image patches regularly in a dense manner on several scales.  ...  The different approaches are evaluated on recent image retrieval and fine-grain classification benchmarks.  ...  He is a PhD student at Inria Rennes and University of Rennes I, working on large-scale image retrieval. .  ... 
doi:10.1109/tip.2015.2423557 pmid:25879947 fatcat:h5773norb5em5ki3nvv2mxkc3a

Aggregated Deep Feature from Activation Clusters for Particular Object Retrieval

Zhenfang Chen, Zhanghui Kuang, Kwan-Yee K. Wong, Wei Zhang
2017 Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17  
This paper introduces a clustering based deep feature for particular object retrieval. Many object retrieval algorithms focus on aggregating local features into compact image representations.  ...  By not taking spatial location into account, it is possible to have a single cluster covering multiple disconnected regions that correspond to repeated but isolated visual patterns.  ...  of CNN based methods [3, 4, 11-13, 29, 39] have been proposed for extracting discriminative deep activation features for image retrieval.  ... 
doi:10.1145/3126686.3126696 dblp:conf/mm/ChenKWZ17 fatcat:yuvdr3ic2vgs5pxahzzucdmkna

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers [article]

Antoine Miech, Jean-Baptiste Alayrac, Ivan Laptev, Josef Sivic, Andrew Zisserman
2021 arXiv   pre-print
Our objective is language-based search of large-scale image and video datasets.  ...  For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images  ...  We would like to thank Lisa Anne Hendricks for feedback.  ... 
arXiv:2103.16553v1 fatcat:rw2av5leebdx7kcrqowxv6yo54

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 4643-4655 Scalable Deep Hashing for Large-Scale Social Image Retrieval. Cui, H., +, TIP 2020 1271-1284 Similarity-Preserving Linkage Hashing for Online Image Retrieval.  ...  ., +, TIP 2020 3626-3637 Scalable Deep Hashing for Large-Scale Social Image Retrieval. Cui, H., +, TIP 2020 1271-1284 Similarity-Preserving Linkage Hashing for Online Image Retrieval.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence [article]

Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham
2020 arXiv   pre-print
In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning.  ...  their structure for real-world applications.  ...  One problem here is how to find suitable image descriptors for image retrieval.  ... 
arXiv:2006.12567v2 fatcat:snb2byqamfcblauw5lzccb7umy

Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network

Zakaria Laskar, Iaroslav Melekhov, Surya Kalia, Juho Kannala
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
The problem of image retrieval is studied in the context of large city-scale datasets.  ...  Publication III, IV address the problem of image retrieval in the fast global and slow but accurate local matching settings.  ... 
doi:10.1109/iccvw.2017.113 dblp:conf/iccvw/LaskarMKK17 fatcat:qhms7distnf2zprw3r6oqeouqa

OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features [article]

Anton Osokin, Denis Sumin, Vasily Lomakin
2020 arXiv   pre-print
We use dense correlation matching of learned local features to find correspondences, a feed-forward geometric transformation model to align features and bilinear resampling of the correlation tensor to  ...  Differently from the standard object detection, the classes of objects used for training and testing do not overlap. We build the one-stage system that performs localization and recognition jointly.  ...  We would like to personally thank Ignacio Rocco, Relja Arandjelović, Andrei Bursuc, Irina Saparina and Ekaterina Glazkova for amazing discussions and insightful comments, without which this project would  ... 
arXiv:2003.06800v2 fatcat:ua5ugph4nja67ipyle4pbtnpye
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