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Diverse Yet Efficient Retrieval using Locality Sensitive Hashing
2016
Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval - ICMR '16
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In this work, we present a method based on randomized locality sensitive hashing which tries to address all of the above requirements simultaneously. ...
While earlier hashing approaches considered approximate retrieval to be acceptable only for the sake of efficiency, we argue that one can further exploit approximate retrieval to provide impressive trade-offs ...
CONCLUSIONS In this paper, we present an approach to efficiently retrieve diverse results based on randomized locality sensitive hashing. ...
doi:10.1145/2911996.2911998
dblp:conf/mir/RaoJJ16
fatcat:eb2lnpa2ovhtzdole5vp2o5wue
Diverse Yet Efficient Retrieval using Hash Functions
[article]
2015
arXiv
pre-print
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2015 pre-print arXiv:1509.06553v1 fatcat:bxm4rabkmveolkzerrbnbnp2za
In this work, we present a method based on randomized locality sensitive hashing which tries to address all of the above requirements simultaneously. ...
While earlier hashing approaches considered approximate retrieval to be acceptable only for the sake of efficiency, we argue that one can further exploit approximate retrieval to provide impressive trade-offs ...
CONCLUSIONS In this paper, we present an approach to efficiently retrieve diverse results based on randomized locality sensitive hashing. ...
arXiv:1509.06553v2
fatcat:gbfrtzxwvfdo3bbc3uxw7nnzma
Multimedia semantics-aware query-adaptive hashing with bits reconfigurability
2012
International Journal of Multimedia Information Retrieval
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In the past decade, locality-sensitive hashing (LSH) has gained a large amount of attention from both the multimedia and computer vision communities owing to its empirical success and theoretic guarantee ...
in large-scale multimedia indexing and retrieval. ...
The family H is called locality sensitive if it satisfies the following conditions: Definition 1 (Locality-sensitive hashing [2] ) A hashing family H is called (1, c, p 1 , p 2 )-sensitive if the following ...
doi:10.1007/s13735-012-0003-7
fatcat:5mzhax6kdjdyxa7y2ctyhtmne4
Survey Paper on Generating Correlation among Different Modalities by Using Parallel Processing for Cross-Media Retrieval
2017
International Journal for Research in Applied Science and Engineering Technology
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Hashing methods are useful for performing variety of tasks in recent years. Various hashing approaches have been performing retrieve the cross-media information. ...
In the proposed method retrieve cross-media information using multi core processer and multi-threading. ...
Binary coding for the cosine equivalence is fully based on the Locality Sensitive Hashing (LSH). But it is not taken advantage for retrieving the non-negative model of histogram data. ...
doi:10.22214/ijraset.2017.8309
fatcat:5j65haeztfdedoljvqrczo2pxq
Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features
2009
Proceeding of the ACM International Conference on Image and Video Retrieval - CIVR '09
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Our previous shape-based 3D model retrieval algorithm compares 3D shapes by using thousands of local visual features per model. ...
To efficiently compare among large sets of local features, the algorithm employs bag-of-features approach to integrate the local features into a feature vector per model. ...
The locality sensitive hashing in the E2LSH library [1] employs a set of L hash functions that maps d dimensional feature vector onto K dimensional vector. ...
doi:10.1145/1646396.1646430
dblp:conf/civr/FuruyaO09
fatcat:ih4oak5ofnclnjgwzb6rmhsxxe
Towards Codebook-Free: Scalable Cascaded Hashing for Mobile Image Search
2014
IEEE transactions on multimedia
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State-of-the-art image retrieval algorithms using local invariant features mostly rely on a large visual codebook to accelerate the feature quantization and matching. ...
Afterwards, we enhance the matching precision by an efficient verification with the binary signatures of these local features. ...
In image retrieval algorithms using local invariant features, the image matching is achieved via local feature matching between images. ...
doi:10.1109/tmm.2014.2301979
fatcat:obb2vdkcp5cp3axupvesbzfvq4
Scaling Reuse Detection in the Web through Two-way Boosting with Signatures and LSH
2013
Journal of Korea Multimedia Society
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Thus, in this paper, we propose a qSignlsh algorithm, a mechanism for identifying multi-sentence content reuse among documents by efficiently combining sentence-level evidences. ...
The emergence of Web 2.0 technologies, such as blogs and wiki, enable even naive users to easily create and share content on the Web using freely available content sharing tools. ...
Locality Sensitive Hashing Locality-sensitive hashing has been used to support approximate similarity searches in a high dimensional space [13, 14] . ...
doi:10.9717/kmms.2013.16.6.735
fatcat:qnoik7x5mjhf3bngh6z2wwedqq
Image Retrieval Method based on Multi-View Generating and Ensemble Learning
2017
International Journal of Performability Engineering
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Then, MEH uses a traditional hashing method to learn hash function and hash code respectively in each generated view. ...
Compared to the original hashing methods that used as the operator in MEH, our proposed approach improves the retrieval precision over 100% at code size of 16-bit, and 10% at code size of 256-bit. ...
For instance, our method improves the performance of Locality Sensitive Hashing (LSH) and Iterative Quantization (ITQ) over 100% at code size of 16-bit. Our hashing method maintains an approximate complexity ...
doi:10.23940/ijpe.17.05.p10.657669
fatcat:dvcf7gymgrfkzcenvmv2fd5m7q
Twitter's visual pulse
2013
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval - ICMR '13
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Millions of images are tweeted every day, yet very little research has looked at the non-textual aspect of social media communication. ...
We detect duplicates using a graph-based approach in which Locality Sensitive Hashing is applied to local features to efficiently determine feature matches between images. ...
To efficiently assess whether features match, Locality Sensitive Hashing is used to create sketches (compact binary strings) from the features. ...
doi:10.1145/2461466.2461514
dblp:conf/mir/HareSDL13
fatcat:ctrpm4mu4zgblc2tdaewnynqzq
Beyond "Near Duplicates": Learning Hash Codes for Efficient Similar-Image Retrieval
2010
2010 20th International Conference on Pattern Recognition
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Learned-hash keys provide the best result, in terms of both recall and efficiency. ...
In this paper, we present a two-tier similar-image retrieval system with the efficiency characteristics found in simpler systems designed to recognize nearduplicates. ...
These requirements lead us to use compact signatures with Locality Sensitive Hash (LSH) table entries as our only representation for the full database in the first tier. ...
doi:10.1109/icpr.2010.138
dblp:conf/icpr/BalujaC10
fatcat:i4udr3btvfei3kbbtrmetvli5u
Compact hashing for mixed image-keyword query over multi-label images
2012
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval - ICMR '12
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Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. ...
To further enhance the hashing efficiency for such multi-label data, we propose a novel scheme "boosted shared hashing". ...
(SH) [18] and random projection based locality sensitive hashing (LSH) [3] as the baseline algorithms. ...
doi:10.1145/2324796.2324819
dblp:conf/mir/LiuMLC12
fatcat:j6d3672laval7eys36v6zfnyay
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
[article]
2018
arXiv
pre-print
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We evaluate the resulting binary image descriptors on two challenging applications, image matching and retrieval, and achieve state-of-the-art results. ...
In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors ...
UMO-2016/21/D/ST6/01946 as well as Google Sponsor Research Agreement under the project "Efficient visual localization on mobile devices". ...
arXiv:1806.06778v5
fatcat:ucfzk2674ranplj6lnsvomxwvm
Mixed image-keyword query adaptive hashing over multilabel images
2014
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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To enhance the hashing efficiency, we propose a novel scheme "boosted shared hashing". ...
This article defines a new hashing task motivated by real-world applications in content-based image retrieval, that is, effective data indexing and retrieval given mixed query (query image together with ...
Recently locality sensitive hashing methods (LSH) attract much attention owing to its theoretic property. ...
doi:10.1145/2540990
fatcat:vhbxtrjo5zh5fbrdtd4fzai2ru
Multilinear Hyperplane Hashing
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Our theoretical analysis shows that with an even number of random linear projections, the multilinear hash function possesses strong locality sensitivity to hyperplane queries. ...
To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. ...
Locality-sensitive hashing (LSH) [1, 2] pioneered the hash based solution with a good balance between the search performance and computational efficiency. ...
doi:10.1109/cvpr.2016.553
dblp:conf/cvpr/LiuFDLST16
fatcat:ylgakpbr6zf7ddk2kv2vaurdbu
Large-scale retrieval for medical image analytics: A comprehensive review
2018
Medical Image Analysis
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Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity ...
Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. ...
Locality-Sensitive Hashing (LSH) and its 615 variants are the most popular data-independent methods (Gionis et al., 1999; 616 Kulis et al., 2009; Raginsky and Lazebnik, 2009). ...
doi:10.1016/j.media.2017.09.007
pmid:29031831
fatcat:s6jnxawnongufgdngpjeifv3vm
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