Image Classification Technique using Modified Particle Swarm Optimization

Shukran, Mohd Afizi Mohd; Chung, Yuk Ying; Yeh, Wei-Chang; Wahid, Noorhaniza; Zaidi, Ahmad Mujahid Ahmad

doi:10.5539/mas.v5n5p150

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…9 is a key component of a content-based image retrieval (CBIR) system because retrieving a collection of images, which are nearly identical to the query image is its main objective. In an ideal world, image indexing and retrieval systems would not only be able to identify and index relevant features of an image but also be able to retrieve only relevant images matching a query by a human user [13] M. A. M. Shukran et al the upper coefficients which contained detailed information of the image will be eliminated whereas all essential and general information known as lower DCT mained in the compressed image [10]. Improvement over the CBIR system can the integration of the semantic-based image retrieval method.…”

Section: Image Retrieval Systemmentioning

confidence: 99%

An analysis of a discrete cosine transform (DCT)compression technique on low level features of image for image retrieval system

Shukran

Abdullah

Zaidi

et al. 2018

J. Fundam and Appl Sci.

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A digital image is basically formed together information about the file (e.g. location captured, aut keywords. These properties allow database. Those images later can be retrieved from the system by running a text which explained the characteristics of an image. Although retrieving by text method is a common and widely used to find images in a very large database, this method however does not guarantee that the results returned is as described in searching query due to the fact that it is totally dependent on the accuracy of stated image properties. In order to improve the accuracy of the text (CBIR) has been proposed as a complement to the standard method.Keywords: image compression; discrete cosine transformation (DCT); image processing; content-based image retrieval (CBIR) system. A digital image is basically formed together with metadata which describes the image such as information about the file (e.g. location captured, author, date, time and etc.), tags allow images to be stored accordingly to its categories in a can be retrieved from the system by running a text which explained the characteristics of an image. Although retrieving by text method is a common and widely used to find images in a very large database, this method t guarantee that the results returned is as described in searching query due to the fact that it is totally dependent on the accuracy of stated image properties. In order to improve the accuracy of the text-based retrieval method, a Content-Based Image Ret (CBIR) has been proposed as a complement to the standard method.

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Section: Image Retrieval Systemmentioning

confidence: 99%

An analysis of a discrete cosine transform (DCT)compression technique on low level features of image for image retrieval system

Shukran

Abdullah

Zaidi

et al. 2018

J. Fundam and Appl Sci.

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“…The mining of classification rules was investigated in [4], [25], [44]. Image classification with SSO has been studied in [34]. Recent applications of the SSO are the grid-computing reliability and service makespan problem [50], the K-Harmonic Means Problem for Mining Data [48], and the Series-Parallel Redundancy Allocation Problem [47], [49].…”

Section: B Simplified Swarm Optimizationmentioning

confidence: 99%

Simple Probabilistic Population-Based Optimization

Lin

Claub

Middendorf

2016

IEEE Trans. Evol. Computat.

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A generic scheme is proposed for designing and classifying simple probabilistic population based optimization algorithms that use principles from Population-based Ant Colony Optimization and Simplified Swarm Optimization for solving combinatorial optimization problems. The scheme, called Simple Probabilistic Population Based Optimization scheme, identifies different types of populations (or archives) and their influence on the construction of new solutions. The scheme is used to show how Simplified Swarm Optimization can be adapted for solving combinatorial optimization problems and how it is related to Population-based Ant Colony Optimization. Moreover, several new variants and combinations of these two metaheuristics are generated with the proposed scheme. An experimental study is done to evaluate and compare the influence of different population types on the optimization behaviour of Simple Probabilistic Population Based Optimization algorithms, when applied to the Traveling Salesperson Problem and the Quadratic Assignment Problem.

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“…Image classification is the significant portion of digital image assessment. Hence, it is necessary to refine the image features effectively without missing essential color information and to minimize the redundant color information and this could be accomplished through two important techniques, such as supervised and unsupervised image classification 11 . Deep learning has envisioned solving the high dimensional and complex data in data‐driven manner.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it is necessary to refine the image features effectively without missing essential color information and to minimize the redundant color information and this could be accomplished through two important techniques, such as supervised and unsupervised image classification. 11 Deep learning has envisioned solving the high dimensional and complex data in data-driven manner. Such systems gradually generate a feature space that is processed by regularities instead of hand-crafted methods.…”

mentioning

confidence: 99%

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Nadendla

Srikrishna

Rao

2022

Concurrency and Computation

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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

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Image Classification Technique using Modified Particle Swarm Optimization

Cited by 10 publications

References 16 publications

An analysis of a discrete cosine transform (DCT)compression technique on low level features of image for image retrieval system

An analysis of a discrete cosine transform (DCT)compression technique on low level features of image for image retrieval system

Simple Probabilistic Population-Based Optimization

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

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