Image Retrieval Based on Color and Texture Features

Chen, Xiuxin; Zheng, Yuejiu; Yu, Chongchong; Gao, Cheng

doi:10.1109/iih-msp.2013.107

Cited by 1 publication

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. (5) Cloud will return the re-encrypted image retrieval results that is received from the KMC to the authorized query user, shown as 7 in Fig. 1.…”

Section: Encrypted Image Retrieval Resultsmentioning

confidence: 99%

“…If two images are similar, then the distance of them will be very small. Euclidean distance is a type of distance that is typically used to measure the similarity of images, shown as Equation (7). It can calculate the similarity of images accurately.…”

Section: New Approach To Manage Image Similaritymentioning

confidence: 99%

“…Given two image features X = {x 1 , x 2 , ... , x l } and Y = {y 1 , y 2 , ... , y l }, the distance NewDis between X and Y can be calculated as Equation (8). Compared with Euclidean distance, we use u i=1 x i /u and u i=1 y i /u to replace x i and y i respectively in the third part of Equation (7).…”

Section: New Approach To Manage Image Similaritymentioning

confidence: 99%

“…In early years, textbased image retrieval systems implemented by manual tagging image properties could fulfill the requirement of image retrieval. With the growing popularity of Internet users, hundreds of millions of images appear on the Internet per second, the traditional text-based image retrieval becomes gradually impractical, because it consumes prohibitive manpower and financial resources for labeling.. Content-based image retrieval (CBIR) [1][2][3] has been proposed for real world applications which uses the image feature extracted automatically from images, such as colors [4,5], textures [6,7], and shapes [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Shen

Cheng

Zhu

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multiple image sources (i.e., owners) unaddressed.In this paper, we propose a secure CBIR scheme that supports Multiple Image owners with Privacy Protection (MIPP). We encrypt image features with a secure multi-party computation technique, which allows image owners to encrypt image features with their own keys. This enables efficient image retrieval over images gathered from multiple sources, while guaranteeing that image privacy of an individual image owner will not be leaked to other image owners. We also propose a new method for similarity measurement of images that can avoid revealing image similarity information to the cloud. Theoretical analysis and experimental results demonstrate that MIPP achieves retrieval accuracy and efficiency simultaneously, while preserving image privacy.

show abstract

“…1. (5) Cloud will return the re-encrypted image retrieval results that is received from the KMC to the authorized query user, shown as 7 in Fig. 1.…”

Section: Encrypted Image Retrieval Resultsmentioning

confidence: 99%

Section: New Approach To Manage Image Similaritymentioning

confidence: 99%