Content-Based Image Retrieval Using Wavelet Spatial-Color and Gabor Normalized Texture in Multi-resolution Database

Lee, Yong-Hwan; Rhee, Sang-Burm; Kim, Bonam

doi:10.1109/imis.2012.98

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…Though the performance of this method is good, higher computational complexity of Gabor wavelets and Fourier descriptor are the major limitations of this method. Lee et al [3] proposed CBIR based on a weighted combination of color and texture to the Discrete Wavelet Transform (DWT). The advantage of this method is the average retrieval time is small.…”

Section: Related Workmentioning

confidence: 99%

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A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

Kishore¹,

Rao²

2020

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Due to the increasing usage of multimedia and storage devices accessible, searching for large image databases has become imperative. Furthermore, the handiness of high-speed internet has escalated the exchange of images by users enormously. Content-Based Image Retrieval is proposed in this work, taking features based on Exact Legendre Moments, HVS color quantization with dc coefficient and statistical properties such as variance, mean, and skew of Conjugate Symmetric Sequency Complex Hadamard Transform (CS-SCHT). In most of the machine learning tasks, the quality of the learning process depends on dimensionality. High dimensional datasets can influence the classification outcome and training time. To overcome this problem, we use DE (Differential Evolution) to generate the optimal feature subsets. The features scaled by weights derived from the firefly algorithm, which fed to Multi-Class SVM. The fitness function taken for the firefly algorithm is the classification error of SVM. By minimizing fitness function, optimum weights are obtained. When these optimal weights are applied to SVM, the proposed algorithm exhibits better precision, recall, and accuracy when compared to some of the existing algorithms in the literature.

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Section: Related Workmentioning

confidence: 99%

“…An image is characterized by a set of features, viz., texture, color, and shape. The feature consists of the hash extracted from a given image that can uniquely identify the image [3]. The features are computed to the entire database images and stored in a feature database for matching and indexing.…”

Section: Feature Databasementioning

confidence: 99%

A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

Kishore¹,

Rao²

2020

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“…The business Internet web crawler Yahoo professes to have ordered about 1.6 billion pictures. This fast development triggers the interest of subjective and quantitative image retrieval frameworks [1]- [3]. To date, different business frameworks and Internet web indexes features for keyword-based image retrieval.…”

Section: Introductionmentioning

confidence: 99%

A State of Art on Content Based Image Retrieval Systems

2019

ijrte

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The digital image data is quick expanding in capacity and heterogeneity. The customary information retrieval approaches are cannot fulfill the client's need, so there isneed to present a proficient framework for Content Based Image Retrieval(CBIR). The CBIR is an appealing wellspring of precise and quick retrieval. CBIR goes for discovering imagedatabases for explicit images that are like a given query image dependent on its features.In this paper the methodology of content based image retrieval are examined, investigated and thought about. Here, the different image substance, for example, colour, texture and shape features are mined by utilizing differentfeature extraction procedures, and furthermore extraordinary distance measures, Relevance Feedback (RF) and indexing methods are used to improve the execution of the CBIR system.The existing exploration strategies are talked about with their benefits and negative marks, so the further research works can be focused more.

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Content-Based Image Retrieval Techniques: A Review

Tyagi

2017

Content-Based Image Retrieval

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Content-Based Image Retrieval Using Wavelet Spatial-Color and Gabor Normalized Texture in Multi-resolution Database

Cited by 9 publications

References 7 publications

A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

A State of Art on Content Based Image Retrieval Systems

Content-Based Image Retrieval Techniques: A Review

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