• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.127-132
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• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• Fashion & Textile Research Journal
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• v.6 no.5
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• pp.549-557
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• 2004

The purpose of this study is to investigate design properties affecting in evaluated image by adult women's town wear in un-limited circumstance. The stimulus, adult women's town wear, were collected from shopping mall, department stores and churches and evaluated by 20's 150 people. Through the estimations, the 76 pictures of 'good image' and 65 pictures of 'bad image', were selected and analyzed by classification categories. The results were as follows : 1) 'Good Image' is classified 6 groups which are like active casual, feminine casual, adult casual, modern, sporty casual, and elegance. 2) "Bad Image' is classified 5 groups which are like easy casual, active casual, soft casual, modern casual, and feminine casual. 3) Central code of adult women's town wear from 'Good Image' are simple, bright, and harmony and 'Bad Image' are complicate, heavy, and inharmony. The coordination, how to wear, is very important to evaluate image of women's town wear with other properties. Also, body shape appeared by important variable in evaluation.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.2
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• pp.155-168
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• 1992

The purposes of this study were: (1) to identify the image dimensions of apparel stores according to how the consumers rate the importance of store attributes; (2) to classify the apparel stores in Seoul, Korea according to consumers' perception of the image attributes of their preferred store; (3) to develop a positioning map of the apparel stores according to their salient image dimensions; and (4) to classify the female adults in Seoul according to the criteria of their preferred store and to describe the characteristics of target customers according to storetype. 'A questionnaire was developed to measure store patronage, perceived importance of the store image attributes, perception of the store image attributes for the respondent's most frequently patronized store, and demographic information. Data from 520 female adults living in Seoul were analyzed. The results were as follows; 1. The image dimensions of fashion stores were product quality, shopping convenience, location, promotion, atmosphere, product information, design characteristics and price. 2. The apparel stores in Seoul were classified into five groups by the perception of store image, which were labeled as national chain store, designer store, specialty store, wholesale store and independent store, according to their discriminant characteristics. 3. According to the positioning map, product quality and location convenience were identified as the most important apparel store type patronage criteria. 4. The female adult group divided by store preference indicated significant differences in the perceived importance of store attributes. Each group showed multi-store patronage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.372-373
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• 2011

인터넷 매체가 급증함에 따라 많은 이들에게 쉽게 노출 되어 유포되고 있는 유해 영상을 검출하기 위해 다양한 분류 방법에 대한 연구들이 이루어지고 있다. 본 논문에서 유해 영상 내의 피부색 영역에서의 Haar-like 특징을 추출하여 유해 영상을 분류하는 방법을 제안한다. 이를 위해, 첫 번째 단계에는 샘플 영상에 대하여 기존에 제안된 피부색 검출 방법을 적용하고, 두 번째 단계에는 검출된 피부색 영역 내의 Haar-like 특징을 추출한다. 각 샘플 영상에서 추출한 특징들은 SVM(Support Vector Machine)을 이용하여 각각 2000 장의 유해, 무해 영상을 학습한다. 학습된 모델은 유해 및 무해 영상이 혼합되어 있는 영상 집합들을 분류하는데 사용한다.

• Journal of Platform Technology
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• v.10 no.1
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• pp.20-28
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• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.