SBIR Phase I: Filtering Objectionable Imagery

这个小型企业创新研究第一阶段项目描述了一种新的创新方法，用于过滤互联网上发布的令人反感的图像内容。随着工作场所互联网使用量的增加，公司不得不应对诸如生产力损失、不必要的带宽使用以及因员工查看令人反感的图像内容而承担的法律责任等问题。该项目提供了一种自动化、智能的对象识别解决方案，可以根据肤色以外的属性来理解令人不快的图像内容。这项研究利用屡获殊荣的机器学习技术来识别图像中的对象。该方法在行业内被广泛认为是能够通过图像属性的光谱和空间处理来识别对象的自适应软件代理技术的第一个实例。该项目将利用这项技术来开发可视内容过滤器。该系统不仅能够过滤令人反感的图像，它还将提供过滤任何图像内容的解决方案，或者反过来，提供用户特定的配置文件来查找用户选择的图像。随着工作场所互联网使用量的持续增加，这项研究对政府和私营行业都有很大的好处。预期的结果是一个适应性强的商业系统，它将不断改进从数据库中过滤不良图像的工作。

This Small Business Innovation Research Phase I project describes a new and innovative approach for filtering objectionable image content distributed on the Internet. As Internet usage in the workplace has increased, companies have had to deal with issues such as loss of productivity, unnecessary bandwidth usage and legal liability due to employees viewing objectionable image content. This project provides an automated, intelligent, object recognition solution that understands objectionable image content based on attributes beyond, for example, skin color. The research leverages the award-winning machine learning technology for object-recognition in imagery. The methodology is widely recognized within the industry as the first instance of an adaptive software agent technology capable of object recognition through both spectral and spatial processing of image attributes. This project will leverage this technology to develop a visual content filter. The system will not only be able to filter objectionable imagery, it will also provide solutions for filtering any image content, or inversely, providing a user-specific profile for finding imagery of a user's choice. As Internet usage in the workplace continues to increase, the research has strong benefits for government and private industry. The anticipated results are an adaptable commercial system that will continually improve the filtering of objectionable imagery from the database.