Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Real-time Computerized Annotation of Pictures
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Real-time Computerized Annotation of Pictures
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通讯作者：
Jia Li;James Ze Wang
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作者：
Jia Li;James Ze Wang
- 开发有效的方法来自动标注数字图片继续挑战计算机科学家。通过计算机注释图片的能力可以在广泛的应用中取得突破，包括Web图像搜索，在线图片共享社区和科学实验。在这项工作中，作者开发了新的优化和估计技术，以解决机器学习中的两个基本问题。这些新技术为全自动、高速在线图片标注的图片实时自动语言索引（ALIPR）系统奠定了基础。特别是，D2聚类方法，在相同的精神，为向量的k-均值，被开发成组的加权向量袋表示的对象。此外，一个广义的混合建模技术（核平滑作为一个特殊情况）的非矢量数据开发使用的新概念的假设局部映射（HLM）。ALIPR已经通过来自互联网照片共享网站的数千张图片进行了测试，这些图片与培训过程中使用的图片来源无关。它的性能也在一个在线演示网站上进行了研究，任意用户提供他们选择的图片，并指出每个注释单词的正确性。实验结果表明，一个单一的计算机处理器可以建议注释条款的实时性和良好的准确性。
— Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the Automatic Linguistic Indexing of Pictures-Real Time (ALIPR) system of fully automatic and high speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as k-means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for non-vector data is developed using the novel concept of Hypothetical Local Mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real-time and with good accuracy.