Image Retrieval using Perceptual Hashing

Sarohi, Harsh Kumar

doi:10.9790/0661-0913840

Cited by 11 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This index is established mainly by image grey value. Grey match can determinate the similarity between two pictures resorting to some measures, for example, correlation function, covariance function, mean square error, etc.. Perceptual hash algorithm [4] is one of the most representative algorithms. The image processing is as follows according to this algorithm.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

Design and Implementation of Image Search Algorithm

Wei¹

2014

AJSEA

View full text Add to dashboard Cite

Abstract:Image search is becoming an urgent problem of the next generation of search engine. We firstly review the developed situation of image search engine in this paper. Then, the main difficulty and key technologies about this engine are analyzed. Next, the design method is elaborated in detail, which mainly includes image recognition, perceptual hash algorithm, system solution, image retrieval procedure as well as software module, and so on. As a result, we develop an image search engine according to above design methods and implement searching image on the Internet. The testing results finally prove the overall performance of our image search engine is excellent and achieves the desired design requirements. By using data filtering technology and perceptual hash algorithm, the search time-consumed is less than 1 second and is of high search efficiency.

show abstract

Section: Image Processing Algorithmmentioning

confidence: 99%

Design and Implementation of Image Search Algorithm

Wei¹

2014

AJSEA

View full text Add to dashboard Cite

show abstract

“…Perceptual hashing is a method to map multimedia data into hash sequence, which is robust to content retention operations and sensitive to content tampering operations [17]. It is widely used in information retrieval [18][19][20], data authentication [21][22][23], copy detection [24,25] and other fields. The realization of perceptual hash is as follows: The data sender generates the hash sequence by feature extraction and feature compression, and the data receiver verifies the integrity by comparing the similarity of the hash sequence.…”

mentioning

confidence: 99%

High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

Zhang

Yan

Zhang

et al. 2020

IJGI

View full text Add to dashboard Cite

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

show abstract