Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

Tian, Ying; Fang, Ming; Kaneko, Shun’ichi

doi:10.3390/math10132196

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Color features are typical local features, and they are also the most easily observed and calculated underlying features [22]. A common method of identifying color histograms is to interpret categories by comparing the proportions of different colors in the entire image [23]. This method cannot determine the spatial position of each color in the image, and is less effective in identifying images that are spectrally similar but have large differences in distribution.…”

Section: Methods Based On Intuitive Featurementioning

confidence: 99%

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Guo,

Jiang,

Gao

et al. 2023

Sensors

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Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the “summation” way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios.

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Section: Methods Based On Intuitive Featurementioning

confidence: 99%

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Guo,

Jiang,

Gao

et al. 2023

Sensors

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“…FCH checks similar colours through the membership value of each pixel and the distribution of pixels across all histogram bins. The fuzzy colour histogram method is used for colour distribution from the point of view of probability [41]. The fuzzy colour histogram method distributes the colour from the probability viewpoint.…”

Section: Ffnn With Fused Cnn and Handcrafted Featuresmentioning

confidence: 99%

Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features

et al. 2023

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White blood cells (WBCs) are one of the main components of blood produced by the bone marrow. WBCs are part of the immune system that protects the body from infectious diseases and an increase or decrease in the amount of any type that causes a particular disease. Thus, recognizing the WBC types is essential for diagnosing the patient’s health and identifying the disease. Analyzing blood samples to determine the amount and WBC types requires experienced doctors. Artificial intelligence techniques were applied to analyze blood samples and classify their types to help doctors distinguish between types of infectious diseases due to increased or decreased WBC amounts. This study developed strategies for analyzing blood slide images to classify WBC types. The first strategy is to classify WBC types by the SVM-CNN technique. The second strategy for classifying WBC types is by SVM based on hybrid CNN features, which are called VGG19-ResNet101-SVM, ResNet101-MobileNet-SVM, and VGG19-ResNet101-MobileNet-SVM techniques. The third strategy for classifying WBC types by FFNN is based on a hybrid model of CNN and handcrafted features. With MobileNet and handcrafted features, FFNN achieved an AUC of 99.43%, accuracy of 99.80%, precision of 99.75%, specificity of 99.75%, and sensitivity of 99.68%.

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“…In contrast to our prior work [21][22][23], instead of directly exploiting all pixels as color features, we select two other inner layers as candidates to enhance color features. Here, we analyze the color distribution in the given dataset to determine the maximum range relationship of each color channel called total color space (TCS).…”

Section: Introductionmentioning

confidence: 99%

Robust Template Matching Using Multiple-Layered Absent Color Indexing

Wei

Tian

Kaneko

et al. 2022

Sensors

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Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

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Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

Cited by 3 publications

References 36 publications

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features

Robust Template Matching Using Multiple-Layered Absent Color Indexing

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