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A Survey of Feature Extraction Techniques in Content-Based Illicit Image Detection

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View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Universiti Teknologi Malaysia Institutional Repository Journal of Theoretical and Applied Information Technology 10 th May 2016. Vol.87. No.1 © 2005 - 2016 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 A SURVEY OF FEATURE EXTRACTION TECHNIQUES IN CONTENT-BASED ILLICIT IMAGE DETECTION 1,2 S.HADI YAGHOUBYAN, 1MOHD AIZAINI MAAROF, 1ANAZIDA ZAINAL, 1MAHDI MAKTABDAR OGHAZ 1Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia 2 Department of Computer Engineering, Islamic Azad University, Yasooj Branch, Yasooj, Iran E-mail: 1,2 [email protected], [email protected], [email protected] ABSTRACT For many of today’s youngsters and children, the Internet, mobile phones and generally digital devices are integral part of their life and they can barely imagine their life without a social networking systems. Despite many advantages of the Internet, it is hard to neglect the Internet side effects in people life. Exposure to illicit images is very common among adolescent and children, with a variety of significant and often upsetting effects on their growth and thoughts. Thus, detecting and filtering illicit images is a hot and fast evolving topic in computer vision. In this research we tried to summarize the existing visual feature extraction techniques used for illicit image detection. Feature extraction can be separate into two sub- techniques feature detection and description. This research presents the-state-of-the-art techniques in each group. The evaluation measurements and metrics used in other researches are summarized at the end of the paper. We hope that this research help the readers to better find the proper feature extraction technique or develop a robust and accurate visual feature extraction technique for illicit image detection and filtering purpose. Keywords: Feature Extraction, Feature Detector, Feature Extract, Illicit Image, Internet Filtering 1. INTRODUCTION The fundamental step in content-based illicit image detection is extracting Visual Features from In today's world, the Internet became an effective these images. Due to the importance of the matter means in the world that leads to a huge revolution and lack of a comprehensive study in the field, we in people communicating and making business. are motivated to prepare a survey on different visual Different from any other communication medium, it feature extraction techniques on illicit images. The has a great effect to the communities and given an term Feature or Visual Feature which also known International dimension to the world. For many of as Keypoint refer to interest image primitives and today’s youngsters and children, the Internet, structures such as edge, corner, blob and etc. They mobile phones and generally digital devices are are containing the most informative data from an integral part of their life and they can barely image and they are very important within the field imagine their life without a social networking of image processing and computer vision. The systems, online gaming, photographs and videos method and technique of identifying these features sharing [1]–[3]. As much as the positive impact of are named Feature Detector . Once features are Internet is noticeable, it is hard to neglect its detected, it is required to represent them negative impacts. Distributing the illicit contents numerically using Feature Descriptor techniques. and more specific the illicit images is one of the The Feature Extraction actually consists of these most significant negative impacts of the Internet. two main steps Feature Detection and Feature Exposure to illicit contents is very common among Description . In other words, Feature Extraction adolescent and children, with a variety of refers to identifying the meaningful information and significant and often upsetting effects on their features from an image using feature detectors and growth and thoughts [4]. These reasons motivates represents them numerically by feature descriptors. the researchers to develop new methods and Feature extraction techniques are engaged to techniques to counter with ever-growing illicit discover the image anomalies and discontinuities in contents. order to recognize the semantic of an image. 110 Journal of Theoretical and Applied Information Technology 10 th May 2016. Vol.87. No.1 © 2005 - 2016 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 Indeed, these anomalies might give a clue to predict following describes each type of features in more the semantic of an image. details. The following sections explain different types of features and then the categorized of detecting 2.1 Global Features techniques based on these features are performed. Global feature are evaluated over the whole Feature description techniques afterward is image or a sub-area of image. Generally, global presented in more details and exiting state-of-the-art features presents statistical facts of the image and descriptors in the field are explained. Finally, the they are able to generalize the entire image by a evaluation metrics and datasets use for evaluate single vector. Resolution, image size, dimensions, visual feature extraction are reported. and aspect ratio are some examples of spatial-based global features. The image moments and average image intensity are some semantic-based global 2. VISUAL FEATURES TYPES IN features. These features have been used to evaluate CONTENT-BASED ILLICIT IMAGE images in various research fields. For example, Generally, in computer vision society, a Feature image contents are described by colour histograms is referred to a function of one or more in image retrieval applications, although the measurements, each of which identifies some foreground and background are mixed together. informative data and quantifiable property of an Many researchers such as [5] [6][7][8] and etc. used object in image. There have been remarkable works global features for sake of illicit image detection. on different approaches to extract several kinds of Global features have some limitations such as features in these images. From image structure dealing with background clutter and occlusion, perspective these approaches could be classified as consequently misleads the feature extraction global features, pixel-level features and local performance. features. Figure 1 shows different types of features used in the literature to detect illicit images. The Content-Based Illicit Image Features Pixel-based FEatures Local Features Global Features Corner Edge Blob Figure 1: Different Types Of Features In Content-Based Illicit Image Detection 111 Journal of Theoretical and Applied Information Technology 10 th May 2016. Vol.87. No.1 © 2005 - 2016 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 2.2 Pixel-level Feature Shen et al. [16] used local feature for sake of Pixel-level features are evaluating each pixel breast and pubes detection in illicit images. individually. Pixel position and pixel intensity (gray Diversity in shape, color and breast size of different level) are two prominent pixel-level features. Each individuals, makes feature extraction as a pixel in image carries its Spatial-Positioning challenging task. The other study by Chung et information which are represented as pair of scalar al.[17] used the skin textural features to detect the (x, y). These pairs specify the offset of a particular obscene objects in low quality images. The main pixel from the image origin i.e. in image-processing problem of this technique is that textural features the image origin is the top-left corner of the image. are tend to fade away in low quality images. A very Special information of pixels might bring useful similar study by Li et al. [18] used texture and information when the occurrence of the particular shape features to classify illicit images. color cluster is a function of its position. Beside Mofaddel and Sadek [19] also took advantage of pixel position, each pixel has a pixel intensity local features such as edges detection in order to which specifies the value that the corresponding spot the illicit images. They believe that the number pixel carries to represent its illumination and of the edges in the connected skin region helps to chromaticity [9]. Meanwhile, the intensity feature detect illicit images. The authors assumed that skin could have different structure that depends on used regions are tending to contain less edges compare to image color space. For example, the RGB colour other areas. In the other work Zeng et al [20] space presents pixel intensities in range 0 to 255 utilized local feature such as shape features, texture which they are identified by three values Red, coarseness and texture contrast in order to spot Green and Blue [10]. illicit images. In a relatively different fashion Zhang Pixel-level features are unable to directly present et al. [21] used Bag of Visual Word model the sophisticated and high-level structures such as (BoVW) to detect illicit images. A mixture of local area, shape, texture and etc. but these features are and pixel based features including intensity, color, forming the basis for more informative and skin, and texture were extracted in illicit regions. sophisticated features. Despite this fact, the pixel- More recently Zaidan et al. [7] used combination of level features have been utilized in many illicit global , pixel-level and local features in order to image detection techniques as a part of designed detect the illicit images. feature vector. For example, skin color information Since the local feature are the most common and as primary pixel-level feature is indispensable part important feature type in content-based illicit image of many illicit image detector techniques. A number detection techniques, they are explained in more of researches such as [11][5][12][7][13] have been details in the next section separately. used pixel-level features for sake of illicit image detection. To tackle the limitations and weaknesses of global features and pixel-level features, the local features were developed.
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