A Novel System for Detecting Adult Images on the Internet

Park, Jae Yong

doi:10.3837/tiis.2010.10.012

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The method of using color and texture features [13] detects harmful contents in three stages. In the first stage, IOI is extracted from the background of the input image.…”

Section: Related Workmentioning

confidence: 99%

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Harmful Content Detection Based on Cascaded Adaptive Boosting

Jang

Lee

2018

Journal of Sensors

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Recently, it has become very easy to acquire various types of image contents through mobile devices with high-performance visual sensors. However, harmful image contents such as nude pictures and videos are also distributed and spread easily. Therefore, various methods for effectively detecting and filtering such image contents are being introduced continuously. In this paper, we propose a new approach to robustly detect the human navel area, which is an element representing the harmfulness of the image, using Haar-like features and a cascaded AdaBoost algorithm. In the proposed method, the nipple area of a human is detected first using the color information from the input image and the candidate navel regions are detected using positional information relative to the detected nipple area. Nonnavel areas are then removed from the candidate navel regions and only the actual navel areas are robustly detected through filtering using the Haar-like feature and the cascaded AdaBoost algorithm. The experimental results show that the proposed method extracts nipple and navel areas more precisely than the conventional method. The proposed navel area detection algorithm is expected to be used effectively in various applications related to the detection of harmful contents.

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“…The method of using color and texture features [13] detects harmful contents in three stages. In the first stage, IOI is extracted from the background of the input image.…”

Section: Related Workmentioning

confidence: 99%

“…A region feature classifier is then used to classify input images into harmless or harmful by checking the ratio of the skin color region detected previously and the position feature. In [13], harmful contents are detected in three stages. In the first stage, image of interest (IOI) is extracted from the background.…”

Section: Introductionmentioning

confidence: 99%

Harmful Content Detection Based on Cascaded Adaptive Boosting

Jang

Lee

2018

Journal of Sensors

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“…Moreover, Wang et al [29] [30] adopt image annotation technique to video annotation. In addition, image annotation is also used to some special domain [31] [32].…”

Section: … …mentioning

confidence: 99%

A Fair-Exchange E-Payment Protocol For Digital Products With Customer Unlinkability

Yen¹,

Wu²,

Lo³

et al. 2012

KSII TIIS

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Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

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Robust Detection of Body Areas Using an Adaboost Algorithm

Jang¹,

Byun²

2016

Journal of the Korea Academia-Industrial cooperation Society

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Recently, harmful content (such as images and photos of nudes) has been widely distributed. Therefore, there have been various studies to detect and filter out such harmful image content. In this paper, we propose a new method using Haar-like features and an AdaBoost algorithm for robustly extracting navel areas in a color image. The suggested algorithm first detects the human nipples through color information, and obtains candidate navel areas with positional information from the extracted nipple areas. The method then selects real navel regions based on filtering using Haar-like features and an AdaBoost algorithm. Experimental results show that the suggested algorithm detects navel areas in color images 1.6 percent more robustly than an existing method. We expect that the suggested navel detection algorithm will be usefully utilized in many application areas related to 2D or 3D harmful content detection and filtering.

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A Novel System for Detecting Adult Images on the Internet

Cited by 3 publications

References 21 publications

Harmful Content Detection Based on Cascaded Adaptive Boosting

Harmful Content Detection Based on Cascaded Adaptive Boosting

A Fair-Exchange E-Payment Protocol For Digital Products With Customer Unlinkability

Robust Detection of Body Areas Using an Adaboost Algorithm

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