Robust image hash function using local color features

Tang, Zhenjun; Zhang, Xianquan; Xuan, Dong; Yang, Jing; Wu, Tong

doi:10.1016/j.aeue.2013.02.009

Cited by 50 publications

(34 citation statements)

References 10 publications

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“…Many works related to CBIR are published such as extracting object on image based on histogram value (Wang, Wu, & Yang, ), similar image retrieval based on matching interest regions (Bartolini, Ciaccia, & Patella, ; Wang et al, ), colour image retrieval based on bit plane and colour space L*a*b* (Wang, Yang, Li, & Yang, ), converting colour space and building hashing in order to retrieve content of colour images (Tang, Zhang, Dai, Yang, & Wu, ), and so forth.…”

Section: Related Workmentioning

confidence: 99%

Content‐based image retrieval based on binary signatures cluster graph

2017

Expert Systems

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In this paper, we approach a method of clustering binary signature of image in order to create a clustering graph structure for building the content‐based image retrieval. First, the paper presents the segmentation method based on low‐level visual features including colour and texture of image. On the basis of segmented image, the paper creates binary signature to describe location, colour, and shape of interest objects. In order to match similar images, the paper presents a similarity measure between the images based on binary signature. From that, the paper proposes the method of clustering binary signature to quickly query similar images. At the same time, the graph data structure is built using the partition cluster technique and the rules of binary signatures' distribution of images. On the basis of data structure, we propose a graph creation algorithm, a cluster splitting/merging algorithm, and a similarity image retrieval algorithm. To illustrate the proposed theory, we build an image retrieval application and assess the experimental results on the image datasets including COREL (1,000 images), CBIR images (1,344 images), WANG (10,800 images), MSRDI (15,720 images), and ImageCLEF (20,000 images).

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Section: Related Workmentioning

confidence: 99%

Content‐based image retrieval based on binary signatures cluster graph

2017

Expert Systems

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“…From HSI and YCbCr colour space, block mean and variance are obtained. Euclidean distance is calculated between block features and reference features and treated as a image hash [21]. Three histograms are generated for an color image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive CSLBP compressed image hashing

Patil¹,

Sarode²

2019

IJECE

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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.

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“…The inherent contents of the image such as shape, color, texture, etc., are used to retrieve the images from the repository. An exhaustive and detailed description of image retrieval methods is available in Gai et al (2013), Shi et al (2011), Smeulders et al (2000), Tang et al (2013), Rui and Huang (1999). …”

Section: Introductionmentioning

confidence: 99%

Local mesh quantized extrema patterns for image retrieval

Rao

Reddy

2016

SpringerPlus

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In this paper, we propose a new feature descriptor, named local mesh quantized extrema patterns (LMeQEP) for image indexing and retrieval. The standard local quantized patterns collect the spatial relationship in the form of larger or deeper texture pattern based on the relative variations in the gray values of center pixel and its neighbors. Directional local extrema patterns explore the directional information in 0°, 90°, 45° and 135° for a pixel positioned at the center. A mesh structure is created from a quantized extrema to derive significant textural information. Initially, the directional quantized data from the mesh structure is extracted to form LMeQEP of given image. Then, RGB color histogram is built and integrated with the LMeQEP to enhance the performance of the system. In order to test the impact of proposed method, experimentation is done with bench mark image repositories such as MIT VisTex and Corel-1k. Avg. retrieval rate and avg. retrieval precision are considered as the evaluation metrics to record the performance level. The results from experiments show a considerable improvement when compared to other recent techniques in the image retrieval.

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Robust image hash function using local color features

Cited by 50 publications

References 10 publications

Content‐based image retrieval based on binary signatures cluster graph

Content‐based image retrieval based on binary signatures cluster graph

Adaptive CSLBP compressed image hashing

Local mesh quantized extrema patterns for image retrieval

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