Image Clustering with Optimization Algorithms and Color Space

Farshi, Taymaz Rahkar; Demi̇rci̇, Recep; Feizi‐Derakhshi, Mohammad‐Reza

doi:10.3390/e20040296

Cited by 23 publications

(13 citation statements)

References 28 publications

(28 reference statements)

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“…When the number of thresholds increases, the number of cubes will increase. Thus, the number of sub-cubes in the RGB is where the number of threshold was chosen as r [44]. Subsequently, the labels of clusters are distributed as .…”

Section: Image Thresholding and Color Quantizationmentioning

confidence: 99%

Content-Based Image Retrieval with Combined Features: Color and Gradient

Kılıçaslan

Demi̇rci̇

2021

Preprint

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Feature extraction is fundamental stage of effective content based image retrieval (CBIR). However, it remains challenging issue to extract low-level features for retrieval systems. This paper puts forward an effective solution proposal for the aforementioned problem. Initially, images and their gradients are clustered with multi-level thresholding. A codebook is generated with threshold values. The size of the codebook generated depends on the number of thresholds. Consequently, every pixel in color image is included in a cluster by means of the codebook. Color reduction is performed by assigning the average values of pixels in the same cluster. A cluster-based one-dimensional histogram (CBH) is created with the numbers of pixels in every cluster represented with a single color. Then the cluster-based feature vectors with histogram are extracted from original image and gradient image. Accordingly, relevant features are combined. The developed feature vector is called as combined feature vector (CFV). The most important advantages of CFV are that it performs an effective color reduction technique and feature presentation by processing texture information with gradient operator. Therefore, the main contribution of the combined feature vector suggested is its high accuracy and stability for image retrieval. The proposed method has been tested with Corel-1K, Corel-5K Corel-10K and GHIM-10K datasets. In addition, performances of different image histogram similarity techniques such as cosine, histogram intersection and Euclidean distance have been verified with the developed algorithm. Experimental results have been analyzed in two categories. Initially, CBIR results produced with combined feature vectors which are generated by Otsu, Kapur and center of gravity of histogram (CGH) procedures have been evaluated. Then, the CBIR strategy based on CGH method has been compared with CBIR systems with local binary pattern (LBP) and gradient-structures histogram (GSH). It was observed that CBIR approach based on CGH technique has significantly outperformed.

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Section: Image Thresholding and Color Quantizationmentioning

confidence: 99%

Content-Based Image Retrieval with Combined Features: Color and Gradient

Kılıçaslan

Demi̇rci̇

2021

Preprint

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“…Launching of VHR sensors led to the appearance of diverse Pan sharpening methods in recent decades [5][6][7]. In addition, Pan sharpening is a primary image enhancement step for many remote sensing applications, such as object detection [8], change detection [9], image segmentation and clustering [10,11], scene interpretation and visual image analysis [12]. Commonly, image fusion can be classified into three levels-pixel level, feature level and decision or knowledge level-while the Pan sharpening is categorized as a sub-pixel level process [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

2019

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Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

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“…The k-means clustering algorithm [ 9 , 10 , 11 ] is a hard clustering algorithm, which is representative of a typical prototype-based clustering method for objective functions. It utilizes the distance from data points to the prototype as an optimized objective function and an adjustment rule of iterative operation is obtained by using the method of function extreme value evaluation [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Cai

Peng

et al. 2019

Entropy

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Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

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Image Clustering with Optimization Algorithms and Color Space

Cited by 23 publications

References 28 publications

Content-Based Image Retrieval with Combined Features: Color and Gradient

Content-Based Image Retrieval with Combined Features: Color and Gradient

An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

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