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Content-based Image Retrieval With The Normalized Information Distance

机译：归一化信息距离的基于内容的图像检索

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The main idea of content-based image retrieval (CBIR) is to search on an image's visual content directly. Typically, features (e.g., color, shape, texture) are extracted from each image and organized into a feature vector. Retrieval is performed by image example where a query image is given as input by the user and an appropriate metric is used to find the best matches in the corresponding feature space. We attempt to bypass the feature selection step (and the metric in the corresponding feature space) by following what we believe is the logical continuation of the CBIR idea of searching visual content directly. It is based on the observation that, since ultimately, the entire visual content of an image is encoded into its raw data (i.e., the raw pixel values), in theory, it should be possible to determine image similarity based on the raw data alone. The main advantage of this approach is its simplicity in that explicit selection, extraction, and weighting of features is not needed. This work is an investigation into an image dissimilarity measure following from the theoretical foundation of the recently proposed normalized information distance (NID) [M. Li, X. Chen, X. Li, B. Ma, P. Vitanyi, The similarity metric, in: Proceedings of the 14th ACM-SIAM Symposium on Discrete Algorithms, 2003, pp. 863-872]. Approximations of the Kol-mogorov complexity of an image are created by using different compression methods. Using those approximations, the NID between images is calculated and used as a metric for CBIR. The compression-based approximations to Kolmogorov complexity are shown to be valid by proving that they create statistically significant dissimilarity measures by testing them against a null hypothesis of random retrieval. Furthermore, when compared against several feature-based methods, the NID approach performed surprisingly well.
机译：基于内容的图像检索（CBIR）的主要思想是直接搜索图像的视觉内容。通常，从每个图像中提取特征（例如颜色，形状，纹理），并将其组织成特征向量。通过图像示例执行检索，在该示例中，用户输入查询图像作为输入，并使用适当的量度在对应的特征空间中找到最佳匹配。通过遵循我们认为直接搜索视觉内容的CBIR想法的逻辑延续，我们尝试绕过特征选择步骤（以及相应特征空间中的度量）。基于这样的观察，由于最终将图像的整个视觉内容编码到其原始数据（即原始像素值）中，从理论上讲，应该有可能仅根据原始数据确定图像相似性。这种方法的主要优点是其简单性，因为不需要显式选择，提取和加权特征。这项工作是根据最近提出的归一化信息距离（NID）[M. Li，X. Chen，X. Li，B. Ma，P. Vitanyi，相似性度量标准，见：第14届ACM-SIAM离散算法研讨会论文集，2003年，第863-872页]。图像的Kol-mogorov复杂度的近似值是通过使用不同的压缩方法来创建的。使用这些近似值，可以计算图像之间的NID并将其用作CBIR的度量。通过证明针对随机检索的零假设对它们进行了统计上显着的相异性度量，证明了基于压缩的Kolmogorov复杂度近似是有效的。此外，与几种基于特征的方法相比，NID方法表现出令人惊讶的出色。

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