Region-Based Image Retrieval Revisited
Region-Based Image Retrieval Revisited
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DOI：
10.1145/3123266.3123312
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Ryota Hinami;Yusuke Matsui;S. Satoh
中科院分区：
文献类型：
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作者：
Ryota Hinami;Yusuke Matsui;S. Satoh
回顾了基于区域的图像检索(RBIR)技术。在90年代末对RBIR的早期尝试中，研究人员发现了许多方法来指定基于区域的查询和空间关系；然而，当时用颜色直方图等方法来表征区域的方法非常糟糕。在这里，我们通过结合对象的语义规范和空间关系的直观规范来重新审视RBIR。我们的贡献如下。首先，为了支持语义对象规范的多个方面(类别、实例和属性)，我们提出了一个多任务CNN特征，它允许我们使用深度学习技术并联合处理多方面的对象规范。其次，为了帮助用户直观地描述对象之间的空间关系，我们提出了空间关系推荐技术。具体地，通过挖掘搜索结果，系统可以推荐对象之间可行的空间关系。该系统还可以通过基于先前语言的分配的对象类别名称来推荐可能的空间关系。此外，对象级倒排索引支持非常快速的入围列表生成，基于空间约束的重新排序为用户提供即时的RBIR体验。
Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relationships; however, the way to characterize the regions, such as by using color histograms, were very poor at that time. Here, we revisit RBIR by incorporating semantic specification of objects and intuitive specification of spatial relationships. Our contributions are the following. First, to support multiple aspects of semantic object specification (category, instance, and attribute), we propose a multitask CNN feature that allows us to use deep learning technique and to jointly handle multi-aspect object specification. Second, to help users specify spatial relationships among objects in an intuitive way, we propose recommendation techniques of spatial relationships. In particular, by mining the search results, a system can recommend feasible spatial relationships among the objects. The system also can recommend likely spatial relationships by assigned object category names based on language prior. Moreover, object-level inverted indexing supports very fast shortlist generation, and re-ranking based on spatial constraints provides users with instant RBIR experiences.