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Bundle min-hashing for logo recognition
<span title="">2013</span>
<i title="ACM Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bj3ypeajjfeb7pqvvlh5snru4a" style="color: black;">Proceedings of the 3rd ACM conference on International conference on multimedia retrieval - ICMR '13</a>
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We demonstrate the benefits of these techniques for both small object retrieval and logo recognition. ...
We present a scalable logo recognition technique based on feature bundling. Individual local features are aggregated with features from their spatial neighborhood into bundles. ...
BUNDLE MIN-HASHING We build our bundling technique on min-hashing mainly for two reasons: (1) Feature bundles can be naturally represented as sparse sets and (2) min-hashing does not imply a strict ordering ...
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Bundle min-Hashing
<span title="2013-09-04">2013</span>
<i title="Springer Nature">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/prsrkwgmmjas7elf5ki2xrsyvm" style="color: black;">International Journal of Multimedia Information Retrieval</a>
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We present a feature bundling technique based on min-Hashing. Individual local features are aggregated with features from their spatial neighborhood into bundles. ...
We demonstrate the benefits of these techniques for both small object retrieval and logo recognition. ...
Logo recognition Now that we have discussed visual features, vocabularies, feature bundling, re-ranking and synthetic query expansion; we present our final logo recognition system: Indexing The logo classes ...
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Robust Feature Bundling
[chapter]
<span title="">2012</span>
<i title="Springer Berlin Heidelberg">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
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We demonstrate the benefits of these bundles for small object retrieval, i.e. logo recognition, and generic image retrieval. ...
As in practice an exact search for such bundles is infeasible, we employ a robust approximate similarity search with min-hashing in order to retrieve images containing similar bundles. ...
Feature Bundles We build our bundling technique on min-hash mainly for two reasons: (1) Feature bundles can be naturally represented as sparse sets and (2) min-hash does not imply a strict ordering or ...
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Extraction and Recognition of Shoe Logos with a Wide Variety of Appearance Using Two-Stage Classifiers
<span title="2018-05-01">2018</span>
<i title="Institute of Electronics, Information and Communications Engineers (IEICE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xosmgvetnbf4zpplikelekmdqe" style="color: black;">IEICE transactions on information and systems</a>
</i>
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and recognition. key words: logo extraction, logo recognition, maximally stable extremal regions, gradient histogram features, support vector machine ...
In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearance using a limited number of training samples. ...
As another application of LSH for logo detection, Romberg et al. proposed the bundle min-hashing [9] , [10] . ...
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A neural network based place recognition technique for a crowded indoor environment
<span title="">2017</span>
<i title="IEEE">
2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)
</i>
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We also have analyzed the behavior of neural network for changes in hyperparameters and presented the results. ...
We employ cross entropy function to model the cost of our neural network and utilize Adam algorithm for minimizing this cost at the training phase. ...
Romberg and Lienhart have suggested another logo recognition technique using Bundle min-hashing approach [20] . ...
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Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks
[article]
<span title="2016-04-20">2016</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Another benefit of this framework is that it allows for multiple detections of graphic logos using regions that are likely to have an object. ...
recognition rate. ...
Method Learning rate Selective Search mAP Bag of Words (BoW) (tf-idf-sqrt, vocabulary-1000) [3] 0.545 Bundle Min Hashing (1p-wgc-ransac) [3] 0 often than in the Fast mode. ...
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Homographic Class Template for Logo Localization and Recognition
[chapter]
<span title="">2015</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
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We propose a method for localizing and recognizing brand logos in natural images. The task is extremely challenging, due to the various changes in the appearance of the logos. ...
Finally, each class is depicted by the reunion of the suitable keypoints and descriptors, thus leading to a high precision of the proposed logo recognition system. ...
The proposed method falls short for symmetric and circular logos with too few keypoints, and which do not represent well the area, leading to an inability to compute homographies. ...
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Scalable triangulation-based logo recognition
<span title="">2011</span>
<i title="ACM Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bj3ypeajjfeb7pqvvlh5snru4a" style="color: black;">Proceedings of the 1st ACM International Conference on Multimedia Retrieval - ICMR '11</a>
</i>
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We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes. ...
We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. ...
In [30] , Wu et al . bundle local features corresponding to MSER regions and impose simple geometric constraints on the feature bundles. ...
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Scalable Logo Recognition using Proxies
[article]
<span title="2018-11-19">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Logo recognition is the task of identifying and classifying logos. ...
Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations of logos, brands and re-training to cover every variation is impractical. ...
Finally, Ankit Raizada for his work on putting together the PL2K dataset and John Weresh for naming it. ...
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Feature Fusion by Similarity Regression for Logo Retrieval
<span title="">2015</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wsjivbkuezdvxdnrhihbwjrxlu" style="color: black;">2015 IEEE Winter Conference on Applications of Computer Vision</a>
</i>
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We propose a simple yet effective multi-feature fusion approach based on regression models for logo retrieval. ...
Logo class information from the training samples can also be included in the training process by learning an ensemble of regression models for individual logo classes. ...
Bundling min-hash [21] was also proposed to group locally close keypoints and encode them using min-hash. ...
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Instance search retrospective with focus on TRECVID
<span title="2017-02-22">2017</span>
<i title="Springer Nature">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/prsrkwgmmjas7elf5ki2xrsyvm" style="color: black;">International Journal of Multimedia Information Retrieval</a>
</i>
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the small world of the BBC Eastenders series for the last 3 years. ...
The Instance Search (INS) benchmark worked with a variety of large collections of data including Sound & Vision, Flickr, BBC (British Broadcasting Corporation) Rushes for the first 3 pilot years and with ...
The information is shown to be integrated into min-Hash. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13735-017-0121-3">doi:10.1007/s13735-017-0121-3</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28758054">pmid:28758054</a>
<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5531298/">pmcid:PMC5531298</a>
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Recent Advance in Content-based Image Retrieval: A Literature Survey
[article]
<span title="2017-09-02">2017</span>
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arXiv
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We conclude with several promising directions for future research. ...
Numerous techniques have been developed for content-based image retrieval in the last decade. ...
In [126] , geometric min-hashing constructs repeatable hash keys with loosely local geometric information for more discriminative description. In [17] , Wu et al. ...
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Smart Parking: IoT and Blockchain
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<span title="2019-12-03">2019</span>
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arXiv
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Users can pay for their parking tickets using their credit cards from their smartphone app. This decreases their hassle of carrying cash and coins for purchasing parking tickets. ...
Phil Maguire for his guidance, patience, feedback and support during the project. His confidence and interest in this cutting-edge field and the project was always a source of motivation for me. ...
Jodeph Timoney for his diligence and dedication to the Erasmus Mundus MSc in Dependable Software Systems programme. ...
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Large scale near-duplicate image retrieval using Triples of Adjacent Ranked Features (TARF) with embedded geometric information
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<span title="2016-03-19">2016</span>
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arXiv
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This list of candidates is then re-ranked with the help of a geometric verification, using a RANSAC algorithm, for example. ...
This combination of local descriptors is both reproducible and highly discriminative, and thus can be efficiently used for large-scale near-duplicate image retrieval. ...
Among those are bundling features [2] , bundle min-hashing [3] , nested SIFT [4] , geometry-preserving visual phrases [5] , and weak geometrical consistency [6] . ...
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Logo Detection with No Priors
<span title="">2021</span>
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Apart from SIFT, Other approaches have also been explored by the commu-nity, metric learning [54] , [55] , using min-hashing [56] and bundling features for improved search [57] . ...
TOWARDS SMALL OBJECTS: LOGO DETECTION The problem of logo recognition itself has a rich history of research. ...
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