An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

Ershad, Shervan Fekri; Saberi, Mohammad; Tajeripour, Farshad

doi:10.5121/ijma.2012.4305

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…Image retrieval methods can be used in many other computer vision applications, for example skin detection [37], face detection [38], etc. The next step is to apply the proposed algorithm in these areas to improve the versatility of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

et al. 2020

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The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.

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Section: Discussionmentioning

confidence: 99%

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

et al. 2020

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“…These image retrieval techniques can include energy compaction, primitive pattern units, etc. Shervan Fekri-Ershad et al created a feature vector by calculating the element-wise product of the histogram vector in each channel of an image [ 33 ]. Then, by comparing the Euclidean distance between this feature vector and the average feature vector of the entire dataset with a thresh hold, they can extract the skin portion of the image.…”

Section: Methodsmentioning

confidence: 99%

Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention

Bui

Khoi

2022

Sensors

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Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering from skin cancer and more than 7600 of these people will not survive. In the context that doctors at provincial hospitals and health facilities are overloaded, doctors at lower levels lack experience, and having a tool to support doctors in the process of diagnosing skin diseases quickly and accurately is essential. Along with the strong development of artificial intelligence technologies, many solutions to support the diagnosis of skin diseases have been researched and developed. In this paper, a combination of one Deep Learning model (DenseNet, InceptionNet, ResNet, etc) with Soft-Attention, which unsupervisedly extract a heat map of main skin lesions. Furthermore, personal information including age and gender are also used. It is worth noting that a new loss function that takes into account the data imbalance is also proposed. Experimental results on data set HAM10000 show that using InceptionResNetV2 with Soft-Attention and the new loss function gives 90 percent accuracy, mean of precision, F1-score, recall, and AUC of 0.81, 0.81, 0.82, and 0.99, respectively. Besides, using MobileNetV3Large combined with Soft-Attention and the new loss function, even though the number of parameters is 11 times less and the number of hidden layers is 4 times less, it achieves an accuracy of 0.86 and 30 times faster diagnosis than InceptionResNetV2.

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“…Therefore, tissue classification has been considered by many scientific researchers in the last decade. Tissue characteristics can be used in many displacement vision problems and machine learning, such as medical image recognition [2][3], image retrieval [4], object recognition [5], skin recognition [6] and ..... In the last decade, texture classification and texture analysis have been an important topic in the literature on computer vision and image processing sciences.…”

Section: Introductionmentioning

confidence: 99%

Image classification using concatenated of co-occurrence matrix features and local ternary patterns

Kiani

2021

Preprint

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Texture, color, and shape are the three main components that the human visual brain uses to identify or identify environments and objects. Therefore, tissue classification has been considered by many scientific researchers in the last decade. The texture features can be used in many different vision and machine learning problems. As of now, various methods have been proposed for classifying tissues. In all methods, the accuracy of the classification is a major challenge that needs to be improved. This article presents a new method based on a combination of two efficient tissue descriptors, the co-occurrence matrix and local ternary patterns (LTP). First, the local binary pattern and LTP are performed to extract information from the local tissue. In the next step, a subset of statistical properties is extracted from the gray surface concurrency matrices. Finally, the interconnected features are used to teach classification. Performance is evaluated for accuracy on the Brodatz reference data set. The experimental results show that the proposed method offers a higher degree of classification compared to some advanced methods.

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An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

Cited by 14 publications

References 12 publications

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention

Image classification using concatenated of co-occurrence matrix features and local ternary patterns

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