scholarly journals Adaptive CSLBP compressed image hashing

2019 ◽  
Vol 9 (4) ◽  
pp. 2982
Author(s):  
Varsha Patil ◽  
Tanuja Sarode
Keyword(s):  
Hamming Distance ◽  
Color Space ◽  
Image Authentication ◽  
Image Texture ◽  
Weight Factor ◽  
Image Hashing ◽  
Discrimination Capability ◽  
Content Change ◽  
Smooth Region ◽  
Texture Extraction

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.

scholarly journals A Survey on Image Hashing for Image Authentication

2010 ◽  
Vol E93-D (5) ◽  
pp. 1020-1030 ◽  
Author(s):  
Yang OU ◽  
Kyung Hyune RHEE
Keyword(s):  
Image Authentication ◽  
Image Hashing

scholarly journals Robust Image Hashing with Low-Rank Representation and Ring Partition

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenjun Tang ◽  
Zixuan Yu ◽  
Zhixin Li ◽  
Chunqiang Yu ◽  
Xianquan Zhang
Keyword(s):  
Image Quality Assessment ◽  
Characteristic Curve ◽  
Visual Representation ◽  
Image Authentication ◽  
Saliency Map ◽  
Low Rank ◽  
Image Hashing ◽  
Copy Detection ◽  
Social Event Detection ◽  
Low Rank Representation

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.

scholarly journals CBIR Penyakit Kulit dengan Metode Co-Occurrence Matrix dan Color Moments

2017 ◽  
pp. 62
Author(s):  
Ni Putu Chendy Widya Santi ◽  
I Ketut Gede Darma Putra ◽  
I Made Sunia Raharja
Keyword(s):  
Skin Disease ◽  
Color Space ◽  
Texture Feature ◽  
Image Texture ◽  
Research Use ◽  
Color Feature ◽  
Skin Tag ◽  
Moments Method ◽  
Similarity Computation ◽  
Occurrence Matrix

Content Based Image Retrieval (CBIR) is a technique for searching images from database based on information from the image which developed because the technique based on text-based is less effective for represent an image. CBIR skin disease in this research use 12 sample of skin disease images such as Acne, Acropustulosis, Alopecia, Dermatitis, Hemangioma, Herpes, Ichtyosis, Molluscum, Nummular, Skin Tag, Urticaria, and Vitiligo. Method use for this research is for extraction texture feature and color feature from a skin disease image. Texture feature is using co-occurrence Matrix which compute energy, contrast, entropy, homogeneity, and correlation until vector texture result. Extraction color use color moments to compute color space using three moments which result color feature from color distributions such as mean, standard deviation, and skewness. Final result showed the comparison of similarity computation of two methods is the acuration of Color Moments method is more robust than Co-occurrence Matrix Method for skin disease images.

Flotation froth image texture extraction method based on deterministic tourist walks

2017 ◽  
Vol 76 (13) ◽  
pp. 15123-15136 ◽  
Author(s):  
Jianqi Li ◽  
Binfang Cao ◽  
Hongqiu Zhu ◽  
Fangyan Nie
Keyword(s):  
Extraction Method ◽  
Image Texture ◽  
Froth Image ◽  
Texture Extraction

Image authentication based on robust image hashing with geometric correction

2018 ◽  
Vol 77 (19) ◽  
pp. 25409-25429 ◽  
Author(s):  
Ram Kumar Karsh ◽  
Arunav Saikia ◽  
Rabul Hussain Laskar
Keyword(s):  
Image Authentication ◽  
Image Hashing ◽  
Geometric Correction ◽  
Robust Image ◽  
Robust Image Hashing

Infrared polarization image texture extraction via variational decomposition algorithm

2015 ◽  
Author(s):  
Weiguo Zhang ◽  
Jingjing Zhang ◽  
Lina Xun ◽  
Feng Wang ◽  
Hongwu Yuan ◽  
...  
Keyword(s):  
Decomposition Algorithm ◽  
Image Texture ◽  
Polarization Image ◽  
Texture Extraction

RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE

2016 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Siti Khairunniza Bejo ◽  
Nor Hafizah Sumgap ◽  
Siti Nurul Afiah Mohd Johari
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Color Space ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Second Moment ◽  
Variance Homogeneity ◽  
Occurrence Matrix

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522.

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