基于内容的大数据量商标检索系统

到目前为止，大数据量的图像检索依然是一个难题，提出了运行在一种大型数据库上的基于内容的快速的商标图像检索．首先，从商标图像中提取两种统计特征，然后采用概率主成分分析降维，生成特征字典一数据库中商标图像集的一个特征映射．在检索阶段，采用快速的层次检索来得到一个数目不定的候选集，再通过相关反馈进行不断的优化，将候选集的数目减少．直至符合检索要求．在国家商标局提供的30，0270商标图像上运行本系统，每一个查询时间不超过0．3秒．

Content-Based Retrieval System from Huge Trademark Databases

Till now, many trademark retrieval systems have been proposed. Retrieval from huge databases is still a challenging problem. This paper presented a fast content-based retrieval system from huge trademark databases. First, we introduce two appropriate statistical features. In follow, probabilistic principal component analysis (PPCA) is used to reduce feature dimension. In query stage, a fast hierarchical retrieval scheme is taken to get a variable number of candidate set. The query results will be iteratively optimized through relevance feedback. In every iteratiwe process, the size of probability relevance set is reduced to a limited number. Experiment results on a database of 300,270 trademark images demonstrate that the proposed system is fast and efficient. A query process costs only 0.3 seconds