Effective and Efficient Global Context Verification for Image Copy Detection

Zhou, Zhili; Wang, Yunlong; Wu, Q. M. Jonathan; Yang, Ching-Nung; Sun, Xingming

doi:10.1109/tifs.2016.2601065

Cited by 313 publications

(98 citation statements)

References 39 publications

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“…When similar spectral values between the water and its neighbourhood are considered, a possible approach may be integration with the decomposition of mixed pixels to find the best track of the open areas. Furthermore, the contextual information [40] may help the feature connection process.…”

Section: Discussionmentioning

confidence: 99%

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

et al. 2017

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Abstract:This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band-(between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches.

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Section: Discussionmentioning

confidence: 99%

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

et al. 2017

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“…However, other evaluation criteria are not discussed in depth, such as the well-known consistency criterion (e.g., the inferred friend consistency) suggested in work [25]. Besides, as [26] indicates, weight plays an important role in the final evaluation results; however, we do not consider the weight of found friends in this paper for simplicity.…”

Section: Shortcoming Analysesmentioning

confidence: 99%

Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Dou

et al. 2017

Complexity

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With the increasing volume of web services in the cloud environment, Collaborative Filtering-(CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRec SimHash , is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

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“…The problems of copyright protection, authentication, and integrity identification of digital images [1,2] are still a focus of multimedia information security research. Self-embedding watermarking technique is proposed [3] to detect the tampered image areas and recover approximately the tampered content.…”

Section: Introductionmentioning

confidence: 99%

Reference Sharing Mechanism-Based Self-Embedding Watermarking Scheme with Deterministic Content Reconstruction

Niu

Wang

Cheng

et al. 2018

Security and Communication Networks

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This paper presents a reference sharing mechanism-based self-embedding watermarking scheme. The host image is embedded with watermark bits including the reference data for content recovery and the authentication data for tampering location. The special encoding matrix derived from the generator matrix of selected systematic Maximum Distance Separable (MDS) code is adopted. The reference data is generated by encoding all the representative data of the original image blocks. On the receiver side, the tampered image blocks can be located by the authentication data. The reference data embedded in one image block can be shared by all the image blocks to restore the tampered content. The tampering coincidence problem can be avoided at the extreme. The maximal tampering rate is deduced theoretically. Experimental results show that, as long as the tampering rate is less than the maximal tampering rate, the content recovery is deterministic. The quality of recovered content does not decrease with the maximal tampering rate.

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Effective and Efficient Global Context Verification for Image Copy Detection

Cited by 313 publications

References 39 publications

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Reference Sharing Mechanism-Based Self-Embedding Watermarking Scheme with Deterministic Content Reconstruction

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