Content-based image retrieval: A review of recent trends

Hameed, Ibtihaal M.; Abdulhussain, Sadiq H.; Mahmmod, Basheera M.

doi:10.1080/23311916.2021.1927469

Cited by 89 publications

(48 citation statements)

References 141 publications

(164 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another approach is to use Euclidean distance (the distance between 2 points) [1]. This algorithm works on the basic principles of geometry, which allow you to map pixels to pixels.…”

Section: Fig 2 Gaussian Difference (Sift)mentioning

confidence: 99%

“…The rapid development of multimedia technologies, the preservation of high quality images, the improvement of storage technologies contributes to the rapid growth of a large collection of images. This is primarily due to the widespread use of the Internet and portable devices to download digital images [1]. The development of many image retrieval systems requires effective search and browsing tools.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Searching System

Yakubchykt¹,

Yurchak²

2021

ACPS

View full text Add to dashboard Cite

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.

show abstract

“…Another approach is to use Euclidean distance (the distance between 2 points) [1]. This algorithm works on the basic principles of geometry, which allow you to map pixels to pixels.…”

Section: Fig 2 Gaussian Difference (Sift)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Searching System

Yakubchykt¹,

Yurchak²

2021

ACPS

View full text Add to dashboard Cite

show abstract

“…Moments are obtained by using a set of polynomial basis functions. They are used to transform signal from time domain (such as speech) or spatial domain (such as image) into the transform domain [4], [5]. Geometric moments and moments invariants were introduced by [6] to deal with the problem of pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Fast Computation of Hahn Polynomials for High Order Moments

et al. 2022

Self Cite

View full text Add to dashboard Cite

Discrete Hahn polynomials (DHPs) and their moments are considered to be one of the efficient orthogonal moments and they are applied in various scientific areas such as image processing and feature extraction. Commonly, DHPs are used as object representation; however, they suffer from the problem of numerical instability when the moment order becomes large. In this paper, an operative method to compute the Hahn orthogonal basis is proposed and applied to high orders. This paper developed a new mathematical model for computing the initial value of the DHP and for different values of DHP parameters (α and β). In addition, the proposed method is composed of two recurrence algorithms with an adaptive threshold to stabilize the generation of the DHP coefficients. It is compared with state-of-the-art algorithms in terms of computational cost and the maximum size that can be correctly generated. The experimental results show that the proposed algorithm performs better in both parameters for wide ranges of parameter values α and β, and polynomial sizes.INDEX TERMS Hahn polynomials, Hahn moments, propagation error, numerical error.

show abstract

“…However, the accuracy of extracting the significant features in these essential signal processing approaches is crucial [16]. Feature extraction, in particular, is used to reduce the dimensionality of the signals to a finite size [17,18]. Specifically, a finite number of features can be used to represent the signals.…”

Section: Introductionmentioning

confidence: 99%

“…Figure17. The NMSE performance comparing the proposed algorithm and the RAK algorithm[44] with p = 0.3.…”

mentioning

confidence: 99%

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

Al-Utaibi

Abdulhussain

Mahmmod

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter (p) values deviate away from 0.5 to 0 and 1. To this end, this paper proposes a new recurrence relation in order to compute the coefficients of KPs in high orders. In particular, this paper discusses the development of a new algorithm and presents a new mathematical model for computing the initial value of the KP parameter. In addition, a new diagonal recurrence relation is introduced and used in the proposed algorithm. The diagonal recurrence algorithm was derived from the existing n direction and x direction recurrence algorithms. The diagonal and existing recurrence algorithms were subsequently exploited to compute the KP coefficients. First, the KP coefficients were computed for one partition after dividing the KP plane into four. To compute the KP coefficients in the other partitions, the symmetry relations were exploited. The performance evaluation of the proposed recurrence algorithm was determined through different comparisons which were carried out in state-of-the-art works in terms of reconstruction error, polynomial size, and computation cost. The obtained results indicate that the proposed algorithm is reliable and computes lesser coefficients when compared to the existing algorithms across wide ranges of parameter values of p and polynomial sizes N. The results also show that the improvement ratio of the computed coefficients ranges from 18.64% to 81.55% in comparison to the existing algorithms. Besides this, the proposed algorithm can generate polynomials of an order ∼8.5 times larger than those generated using state-of-the-art algorithms.

show abstract

Content-based image retrieval: A review of recent trends

Cited by 89 publications

References 141 publications

Image Searching System

Image Searching System

Fast Computation of Hahn Polynomials for High Order Moments

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

Contact Info

Product

Resources

About