A hardware-accelerated approach to computing multiple image similarity measures from joint histogram

An Entropy-Histogram Approach for Image Similarity and Face Recognition

Mathematical Problems in Engineering ◽

10.1155/2018/9801308 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 4

Author(s):

Mohammed Abdulameer Aljanabi ◽

Zahir M. Hussain ◽

Song Feng Lu

Keyword(s):

Information Theory ◽

Face Recognition ◽

Image Recognition ◽

Similarity Index ◽

Similarity Measures ◽

Image Similarity ◽

Information Theoretic ◽

Joint Histogram ◽

The Face ◽

Index Measure

Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between image intensity values. The joint histogram is based mainly on selecting a set of local pixel features to construct a multidimensional histogram. The proposed approach incorporates the concepts of entropy and a modified 1D version of the 2D joint histogram of the two images under test. Two entropy measures were considered, Shannon and Renyi, giving a rise to two joint histogram-based, information-theoretic similarity measures: SHS and RSM. The proposed methods have been tested against powerful Zernike-moments approach with Euclidean and Minkowski distance metrics for image recognition and well-known statistical approaches for image similarity such as structural similarity index measure (SSIM), feature similarity index measure (FSIM) and feature-based structural measure (FSM). A comparison with a recent information-theoretic measure (ISSIM) has also been considered. A measure of recognition confidence is introduced in this work based on similarity distance between the best match and the second-best match in the face database during the face recognition process. Simulation results using AT&T and FEI face databases show that the proposed approaches outperform existing image recognition methods in terms of recognition confidence. TID2008 and IVC image databases show that SHS and RSM outperform existing similarity methods in terms of similarity confidence.

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Fractal-Based Secured Multiple-Image Compression and Distribution

Handbook of Research on Secure Multimedia Distribution ◽

10.4018/978-1-60566-262-6.ch027 ◽

2011 ◽

pp. 515-532

Author(s):

Hsuan T. Chang ◽

Chih-Chung Hsu

Keyword(s):

Image Compression ◽

Image Similarity ◽

Multiple Images ◽

Image Distribution ◽

Coding Scheme ◽

Multiple Image ◽

Image Compression And Encryption ◽

Novel Design

This chapter introduces a pioneer concept in which multiple images are simultaneously considered in the compression and secured distribution frameworks. We have proposed the so-called fractal mating coding scheme to successfully implement the joint image compression and encryption concept through a novel design in the domain pool construction. With the exploration of the intra- and inter-image similarity among multiple images, not only the coding performance can be improved, but also the secured image distribution purpose can be achieved. The authors hope that the revealed fractal-based ideas in this chapter will provide a different perspective for the image compression and distribution framework.

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Improving image similarity measures for image browsing and retrieval through latent space learning between images and long texts

2010 IEEE International Conference on Image Processing ◽

10.1109/icip.2010.5652827 ◽

2010 ◽

Author(s):

Yoshitaka Ushiku ◽

Tatsuya Harada ◽

Yasuo Kuniyoshi

Keyword(s):

Similarity Measures ◽

Image Similarity ◽

Latent Space ◽

Image Browsing

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A Filter of Minhash for Image Similarity Measures

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0689 ◽

2018 ◽

Vol 22 (5) ◽

pp. 689-698

Author(s):

Jun Long ◽

Qunfeng Liu ◽

Xinpan Yuan ◽

Chengyuan Zhang ◽

Junfeng Liu ◽

...

Keyword(s):

Large Scale ◽

Nearest Neighbor ◽

Similarity Measures ◽

Computation Time ◽

Image Features ◽

Nearest Neighbor Search ◽

Image Similarity ◽

Scale Invariant ◽

Minwise Hashing ◽

Image Datasets

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific thresholdT(e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets.

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