Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

Guo, Jing-Ming; Prasetyo, Heri; Chen, Jen-Ho

doi:10.1109/tcsvt.2014.2358011

Cited by 65 publications

(5 citation statements)

References 65 publications

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“…Based on the query image features are extracted. The feature vectors are used in place of the images as transactions which are then used in the classification or retrieval processes [19]. Retrieved image with conclusion matrix for CBIR lung image in Table 2 and recall rates analysis by varying feature selection techniques explained in Table 3.…”

Section: Expermental Results and Discusionmentioning

confidence: 99%

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Hussein

Abbas²

2021

TELKOMNIKA

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The objective of content-based image retrival (CBIR) is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time. All the proposed approaches are different, yet the research goal is to attain better accuracy in a reasonable amount of time. The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features. The second phase of the research extracts features and the association rules are formed by the proposed classification based on highly strong association rules (CHiSAR). Finally, the rule subset classifier is employed to classify between the images. The last pert of our work extracts the features from the kidney images and the association rules are reduced for better performance. The image relevance inference is performed and finally, binary and the best first search classification is employed to classify between the images.

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Section: Expermental Results and Discusionmentioning

confidence: 99%

Suport visual details of X-ray image with plain information

Hussein

Abbas²

2021

TELKOMNIKA

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“…To ensure that there are enough pixels in the image block, set the minimum size of the image block to 5×5. If the requirements are not met, reduce the minimum size to 100 image blocks, the final number of image blocks shall prevail 22 , 23 …”

Section: Methodsmentioning

confidence: 99%

“…If the requirements are not met, reduce the minimum size to 100 image blocks, the final number of image blocks shall prevail. 22,23…”

Section: Resize Imagementioning

confidence: 99%

(Retracted) Method of defogging unmanned aerial vehicle images based on intelligent manufacturing

et al. 2022

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Intelligent manufacturing is a major trend in manufacturing innovation around the world, and it is also the main direction and key breakthrough point for the transformation and upgrading of the manufacturing industry for a long time now and in the future. Here, we mainly study the role of intelligent manufacturing in defogging unmanned aerial vehicle (UAV) images and how to analyze UAV image defogging methods based on intelligent manufacturing. In recent years, with the continuous progress of UAV technology, UAV project has also been booming. Aerial photographing is one of the most widely used functions of UAV at present. However, when photographing images in severe weather environment such as haze, it will be affected by the absorption and scattering of light by a variety of different suspended substances in the environment, resulting in poor imaging quality, color distortion, fine pitch blur, and other adverse effects. For this reason, there is a large amount of research on the image haze removal of UAV in China. At present, the mainstream methods are based on non-vision sensor and physical model. However, due to the lack of haze removal effect and severe condition limitation in the practical application of the above methods, we explore an innovative set of automatic estimation haze removal method of atmospheric light based on this method, taking the principle of atmospheric estimation direction as the main research direction, and then through the steps of setting the global transmittance, calculating the atmospheric light amplitude, adjusting the image size, and automatically modifying the image condition threshold value, etc. The man-machine image is haze removal. To compare the advantages and disadvantages of the three methods, we choose standard deviation, information entropy, and objective evaluation method to analyze the results of the three methods. Data analysis shows that among the three haze removal methods, the atmospheric light automatic estimation haze removal method has greatly improved the overall haze removal effect. Compared with the previous two kinds of haze removal imaging in the haze environment, it can better guarantee the color balance degree, and also retain more image information, which makes the imaging clarity have a significant improvement, and the overall effect is more natural. This method has played a very good complementary role to the domestic UAV demisting technology.

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“…Colour-based features [36][37][38][39][40][41][42][43] offer fundamental visual information that is similar to human vision, and they are relatively robust against image transformations. Texturebased features [44][45][46][47][48][49][50] capture repeating patterns of local variance in image intensity; these features often hold more semantic meaning than colour-based features, though they can be susceptible to image noise.…”

Section: Related Work 21 Feature Extraction and Relevant Similarity M...mentioning

confidence: 99%

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Zhang,

Cosma,

Bugby

et al. 2023

J. Imaging

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Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.

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Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

Cited by 65 publications

References 65 publications

Suport visual details of X-ray image with plain information

Suport visual details of X-ray image with plain information

(Retracted) Method of defogging unmanned aerial vehicle images based on intelligent manufacturing

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

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