大规模互联网图像检索与模式挖掘

Large-scale web image search and pattern mining

在互联网时代,爆炸式增长的数字图像不仅给图像检索带来巨大的技术挑战,同时也带来了很多机遇和研究问题的新思路.本文简单回顾了图像检索的三个阶段的研究历史,以及在此过程中数据量的增多给图像检索带来的影响,并对作为关键问题的特征提取方面的研究进行了深入的分析.本文尤其指出视觉模式挖掘是寻找中层特征表示并缩小语义鸿沟的重要研究方向,并根据视觉模式的表征粒度将其分为五种类别分别进行了介绍,从中可以看到大数据对于视觉模式挖掘的重要作用.

The explosive growth of web images not only brings many technical challenges to image search, but also provides almost unlimited training data and new ideas to various computer vision problems. This paper presents a brief historical review of three stages of image retrieval, with a particular emphasis on the impact of large-scale web images to image retrieval. Based on the review, the paper discusses the fundamental problem of feature extraction in image retrieval, and the recent research trend on visual pattern mining to bridge the semantic gap. According to their representation granularity, the paper divides visual patterns into five categories and introduces their related work respectively, which also shows the great importance of big data to visual pattern mining.