Explicit Content Image Detection
نویسندگان
چکیده
منابع مشابه
Image Spam Filtering by Content Obscuring Detection
We address the problem of filtering image spam, a rapidly spreading kind of spam in which the text message is embedded into attached images to defeat spam filtering techniques based on the analysis of e-mail’s body text. We propose an approach based on low-level image processing techniques to detect one of the main characterstics of most image spam, namely the use of content obscuring technique...
متن کاملDetection of Mammographic Masses by Content-Based Image Retrieval
Computer-aided diagnosis (CAD) of mammographic masses is important yet challenging, since masses have large variation in shape and size and are often indistinguishable from surrounding tissue. As an alternative solution, content-based image retrieval (CBIR) techniques can facilitate the diagnosis by finding visually similar cases. However, they still need radiologists to identify suspicious reg...
متن کاملContent Based Detection of Popular Images in Large Image Databases
We investigate the use of standard image descriptors and a supervised learning algorithm for estimating the popularity of images. The intended application is in large scale image search engines, where the proposed approach can enhance the user experience by improving the sorting of images in a retrieval result. Classification methods are trained and evaluated on real-world user statistics recor...
متن کاملContent-based Image Retrieval (cbir) System Aided Tumor Detection
Nowadays, automatic defects detection in MRI (Magnetic Resonance image) is very important in many diagnostic and therapeutic applications. This paper introduces a Novel automatic brain tumor detection method to determine any abnormality in brain tissues. Here, a number of features which represent a description of brain tissues are extracted. The retrieval of images based on visual features tech...
متن کاملContent-Based Image Orientation Detection with Support Vector Machines
Accurate and automatic image orientation detection is of great importance in image libraries. In this paper, we present automatic image orientation detection algorithms by adopting both the illuminance (structural) and chrominance (color) low-level content features. The statistical learning Support Vector Machines (SVMs) are used in our approach as the classifiers. The different sources of the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal & Image Processing : An International Journal
سال: 2010
ISSN: 2229-3922
DOI: 10.5121/sipij.2010.1205