Low-level features and deep features each have their own advantages and disadvantages in image representation. However, combining their advantages within a CBIR framework remains challenging. To address this problem, we propose a novel image-retrieval method: the deep-seated features histogram (DSFH). Its main highlights are: 1) Low-level features are extracted by simulating the human orientation selection and color perception mechanisms. This follows the human habit of looking at conspicuous regions and then less-conspicuous ones. 2) A novel method, ranking whitening, is proposed for extracting deep features via low-level features and combining them to obtain deep-seated features. 3) The proposed method is straightforward and reduces the vector dimensionality of the FC7 layer of a pre-trained VGG-16 network, and significantly improves image-retrieval precision. Comparative experiments demonstrate that the proposed method outperforms several state-of-the-art methods, including low-level feature-based, deep feature-based, and fused feature-based methods, in terms of precision/recall, area under the precision/recall curve metrics, and mean average precision. The proposed method provides efficient CBIR performance and not only has the power to discriminate low-level features, including color, texture, and shape, but can also match scenes of similar style.

低级特征和深层特征在图像表示中各有其优缺点。但是，在CBIR框架内结合它们的优势仍然具有挑战性。为了解决这个问题，我们提出了一种新颖的图像检索方法：深层特征直方图（DSFH）。它的主要亮点是：1）通过模拟人类的方向选择和色彩感知机制来提取低级特征。这遵循了人类先注意显眼区域然后再注意不显眼区域的习惯。2）一种新颖的方法，排名美白提出通过底层特征提取深层特征并将其组合以获得深层特征。3）所提出的方法是直接的，并且降低了预训练的VGG-16网络的FC7层的向量维数，并显着提高了图像检索精度。比较实验表明，在精度/召回率，精度/调出曲线指标和平均平均精度。所提出的方法提供了有效的CBIR性能，不仅具有区分低级特征（包括颜色，纹理和形状）的能力，而且还可以匹配相似样式的场景。