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Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

机译:局部平行十字图案:用于图像检索的颜色纹理描述符

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摘要

Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.
机译:在视觉传感器设备(例如个人智能手机,家庭安全摄像机,车载摄像机和便携式摄像机)的浪潮中,图像检索(IR)技术由于其在电子商务,视觉监控和智能交通中的潜在应用而受到越来越多的关注。 。但是,确定如何设计有效的特征描述符已被证明是检索一组感兴趣的图像的主要瓶颈。在本文中,我们首先构造一个六层颜色量化器以提取颜色图。然后,在人类视觉系统的激励下,我们设计了一个本地平行十字图案(LPCP),其中本地二进制图案(LBP)映射与颜色映射以“平行”和“交叉”方式融合在一起。最后,为了降低计算复杂度并提高图像旋转的鲁棒性,将LPCP分别扩展为均匀局部平行十字图案(ULPCP)和旋转不变局部平行十字图案(RILPCP)。在八个基准数据集上进行了广泛的实验。实验结果验证了所提出的描述符相对于八个最新的颜色纹理描述符的有效性,效率,鲁棒性和计算复杂性,从而进行了深入的比较。此外,与一系列基于卷积神经网络(CNN)的模型相比,提出的描述符仍可达到竞争性结果。

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