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Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.


Papers
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Proceedings Article
03 Aug 2013
TL;DR: The experimental results show it is not necessary to update all hash bits in order to adapt the model to new input data, and meanwhile the proposals obtain better or similar performance without sacrificing much accuracy against the batch mode update.
Abstract: Recent years have witnessed the growing popularity of hash function learning for large-scale data search. Although most existing hashing-based methods have been proven to obtain high accuracy, they are regarded as passive hashing and assume that the labelled points are provided in advance. In this paper, we consider updating a hashing model upon gradually increased labelled data in a fast response to users, called smart hashing update (SHU). In order to get a fast response to users, SHU aims to select a small set of hash functions to relearn and only updates the corresponding hash bits of all data points. More specifically, we put forward two selection methods for performing efficient and effective update. In order to reduce the response time for acquiring a stable hashing algorithm, we also propose an accelerated method in order to further reduce interactions between users and the computer. We evaluate our proposals on two benchmark data sets. Our experimental results show it is not necessary to update all hash bits in order to adapt the model to new input data, and meanwhile we obtain better or similar performance without sacrificing much accuracy against the batch mode update.

12 citations

Journal Article
TL;DR: A frame hash based video hash construction framework is proposed, which reduces a video hash design to an image hash design, so that the performance of the video hash can be estimated without heavy simulation.
Abstract: Perceptual hashing is a technique for content identification and authentication. In this work, a frame hash based video hash construction framework is proposed. This approach reduces a video hash design to an image hash design, so that the performance of the video hash can be estimated without heavy simulation. Target performance can be achieved by tuning the construction parameters. A frame hash algorithm and two performance metrics are proposed.

12 citations

Journal ArticleDOI
TL;DR: A hash algorithm that is robust and secure to non malicious manipulation and sensitive to the malicious tampering is proposed that will help in faster and effective retrieval of specific image from image repositories.
Abstract: This paper proposes a new perceptual hashing based approach for Image Retrieval. Information in form of images is increasing at a very fast rate therefore there is need for faster and effective retrieval of specific image from image repositories. Content Based Image Retrieval (CBIR) is considered most efficient to search large image collections. The proposed method uses perceptual hash function which calculates similar hash value for similar images. Finally, using an adequate distance or similarity function to compare two perceptual hash values, it can be decided whether two images are perceptually different or not. Perceptual image hash functions can be used e.g. for the identification, authentication or integrity verification of images. In this paper we also propose a hash algorithm that is robust and secure to non malicious manipulation and sensitive to the malicious tampering. Keywords - Content based Image Retrieval (CBIR), Perceptual Hash algorithm, Image Retrieval using Perceptual Hashing, Query by Image Content (QBIC).

12 citations

Proceedings ArticleDOI
Zhou Yu1, Fei Wu1, Yin Zhang1, Siliang Tang1, Jian Shao1, Yueting Zhuang1 
03 Jul 2014
TL;DR: In this paper, the hashing problem is considered from the perspective of optimizing a list-wise learning to rank problem and an approach called List-Wise supervised Hashing (LWH) is proposed, which obtains a significant improvement over the state-of-the-art hashing approaches due to both structural large margin and list- Wise ranking pursuing in a supervised manner.
Abstract: Hashing techniques have been extensively investigated to boost similarity search for large-scale high-dimensional data. Most of the existing approaches formulate the their objective as a pair-wise similarity-preserving problem. In this paper, we consider the hashing problem from the perspective of optimizing a list-wise learning to rank problem and propose an approach called List-Wise supervised Hashing (LWH). In LWH, the hash functions are optimized by employing structural SVM in order to explicitly minimize the ranking loss of the whole list-wise permutations instead of merely the point-wise or pair-wise supervision. We evaluate the performance of LWH on two real-world data sets. Experimental results demonstrate that our method obtains a significant improvement over the state-of-the-art hashing approaches due to both structural large margin and list-wise ranking pursuing in a supervised manner.

12 citations

Journal ArticleDOI
TL;DR: A robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications and verify that the hashing scheme outperforms conventional hashing schemes.

12 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202289
202111
202016
201916
201838