Efficient Cross-Domain Image Retrieval by Multi-Level Matching and Spatial Verification for Structural Similarity
Efficient Cross-Domain Image Retrieval by Multi-Level Matching and Spatial Verification for Structural Similarity
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DOI：
10.1145/2647868.2655032
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发表时间：
2014-11
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影响因子：
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通讯作者：
Che-Chun Lee;Y. Kuo;Winston H. Hsu;S. Satoh;Sebastian Agethen
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作者：
Che-Chun Lee;Y. Kuo;Winston H. Hsu;S. Satoh;Sebastian Agethen
基于内容的图像检索（CBIR）技术对于快速增长的Web图像浏览具有重要意义。然而，当查询图像和数据库图像处于不同的域时，传统的CBIR方法通常会失败。我们提出了一种解决一般跨域图像检索问题的方法，而不是专注于特定领域。这种方法关注的是图像内容的结构，而不是像素级的细节，因此它对于跨不同视觉域（如绘画、手绘草图或照片）的图像匹配特别有用。为了提供一个高效的解决方案，我们分析了词袋（BoW）匹配过程，找出了导致它在跨域设置下失败的原因。我们发现，在跨域图像检索中，有必要采用不同的匹配约束；因此，我们提出了一种多层次匹配过程，动态选择最合适的匹配约束进行特征匹配，并采用快速的空间验证来描述结构相似度。最后，我们的方法比目前的解决方案要快得多，并且在实验中取得了更好的性能。
Content-based image retrieval (CBIR) technique is important for browsing the rapidly growing Web images. However, traditional CBIR methods usually fail when the query and database images are in different domains. Instead of focusing on a specific domain, we propose a method to solve the general cross-domain image retrieval problem. This method focuses on the structure of image content but not the detail at pixel level, so it is particularly useful for matching the images across different visual domains e.g., paintings, hand-drawn sketches or photographs. To provide an efficient and effective solution, we analyze the bag-of-words (BoW) matching procedure to find out what causing it to fail in the cross-domain setting. We observe that it is necessary to apply different matching constraints for cross-domain image retrieval; therefore, we propose a multi-level matching process which dynamically selects the most suitable matching constraint for feature matching and adopt a fast spatial verification to describe the structure similarity. Finally, our method is much faster than the state-of-the-art solution and achieves better performance in the experiments.