Fine-Grained Image Search
Fine-Grained Image Search
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DOI：
10.1109/tmm.2015.2408566
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发表时间：
2015-03
影响因子：
7.3
通讯作者：
Lingxi Xie;Jingdong Wang;Bo Zhang-;Qi Tian
中科院分区：
文献类型：
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作者：
Lingxi Xie;Jingdong Wang;Bo Zhang-;Qi Tian
大规模图像搜索已经引起了学术界和商业界的广泛关注。传统的视觉词袋（BoVW）模型与倒排索引被证明是有效的检索接近重复的图像，但它是不太能够发现细粒度的概念在查询和返回语义匹配的搜索结果。在本文中，我们建议实例搜索不仅应该返回近似重复的图像，而且应该返回细粒度的结果，这通常是用户的实际意图。我们提出了一个新的和有趣的问题，名为细粒度的图像搜索，这意味着我们更喜欢那些图像包含相同的细粒度的概念与查询。我们制定的问题，通过构建一个分层数据库，并定义一个评估方法。此后，我们引入了一个基线系统，使用细粒度的分类分数来表示和共同索引图像，以便更好地将语义属性纳入在线查询阶段。大规模的实验表明，有前途的搜索结果取得了合理的时间和内存消耗。我们希望这篇论文能成为未来图像搜索工作的基础。我们也期待更多的后续努力沿着这个研究课题，并期待商业化的细粒度图像搜索引擎。
Large-scale image search has been attracting lots of attention from both academic and commercial fields. The conventional bag-of-visual-words (BoVW) model with inverted index is verified efficient at retrieving near-duplicate images, but it is less capable of discovering fine-grained concepts in the query and returning semantically matched search results. In this paper, we suggest that instance search should return not only near-duplicate images, but also fine-grained results, which is usually the actual intention of a user. We propose a new and interesting problem named fine-grained image search, which means that we prefer those images containing the same fine-grained concept with the query. We formulate the problem by constructing a hierarchical database and defining an evaluation method. We thereafter introduce a baseline system using fine-grained classification scores to represent and co-index images so that the semantic attributes are better incorporated in the online querying stage. Large-scale experiments reveal that promising search results are achieved with reasonable time and memory consumption. We hope this paper will be the foundation for future work on image search. We also expect more follow-up efforts along this research topic and look forward to commercial fine-grained image search engines.