Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization

Chen, Yanhong; Chang, Cheng‐Tao; Lin, Chia-Chen; Hsu, Ching‐Chi

doi:10.3390/sym11010021

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Techniques based on BTC are more accurate and have a lower processing cost. "Color Coherence vector" (Pass et al, 1996), "Color Correlogram" (Huang et al, 1997), Block Truncation Coding(BTC) and its variants like "Optimized Dot Diffusion" (Guo & Liu, 2014), "error diffused BTC" , "halftoning based BTC" , "Dot Diffused BTC (DDBTC)" , "ant colony optimization based BTC" (Chen et al, 2018), "Thepade's Sorted BTC(SBTC)," color moment-based techniques (Wang et al, 2018), histogram of colors-based techniques like "MPEG-7 Dominant Color Descriptor" (Shao et al, 2008) and "Fuzzy Histogram" (Han & Ma, 2002) are some of the color feature-based techniques.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Dewan¹,

Thepade²

2023

JST

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Because of the tremendous growth in digital imaging, enhanced communication and storage technology, billions of images are captured, stored, and exchanged daily. Finding and searching for an image in a large collection is becoming challenging. The query by reference image retrieval (IR) technique aims to close the semantic gap between the query and retrieve images while improving performance. The primary goal of the work proposed here is to develop discriminative and descriptive features of the image with the minimum possible size. Here, the weighted feature fusion-based IR technique is proposed using Sauvola local thresholding (SLT) and Thepade’s Sorted Block Truncation Coding (SBTC) methods. The proposed technique is tested using two standard datasets with mean square error (MSE) as a distance measure and average retrieval accuracy (ARA) as a performance metric. The technique has contributed to the enhancement of ARA with the small and fixed-size image feature vector. The feature vector generated is much smaller than the image dimension and is used as a feature vector to represent the image for retrieval. Results prove that the proposed technique of SBTC 8-ary with 0.1 weight and SLT with 0.9 weight feature fusion gives better ARA than other techniques studied.

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Section: Related Workmentioning

confidence: 99%

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Dewan¹,

Thepade²

2023

JST

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“…where, I gt expresses to the ground truth (GT) and I ROI points for the segmented image with the proposed strategy. Other related works implemented on brain MRI can be found in [41][42][43][44][45][46][47][48][49].…”

Section: Tumor Quantization and Validationmentioning

confidence: 99%

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

et al. 2020

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Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.

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“…ere are various extensions of BTC such as "Dot Diffused BTC (DDBTC)" [10], "error diffused BTC" [11], "Optimized Dot Diffusion based BTC" [12], "halftoning based BTC" [13], and "BTC based on ant colony optimization" [14].…”

Section: Color Features Used In Image Retrievalmentioning

confidence: 99%

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Dewan¹,

Thepade²

2020

Applied Computational Intelligence and Soft Computing

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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

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Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization

Cited by 17 publications

References 29 publications

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

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