Bhattacharjee Debotosh, Roy Hiranmoy
IEEE Trans Pattern Anal Mach Intell. 2021 Feb;43(2):595-607. doi: 10.1109/TPAMI.2019.2930192. Epub 2021 Jan 8.
This paper presents a novel local image descriptor called Pattern of Local Gravitational Force (PLGF). It is inspired by Law of Universal Gravitation. PLGF is a hybrid descriptor, which is a combination of two feature components: one is the Pattern of Local Gravitational Force Magnitude (PLGFM), and another is Pattern of Local Gravitational Force Angle (PLGFA). PLGFM encodes the local gravitational force magnitude, and PLGFA encodes the local gravitational force angle that the center pixel exerts on all other pixels within a local neighborhood. We propose a novel noise resistance and the edge-preserving binary pattern called neighbors to center difference binary pattern (NCDBP) for gravitational force magnitude encoding. Finally, the histograms of the two components are concatenated to construct the PLGF descriptor. Experimental results on the existing face recognition databases, texture database, and biomedical image database show that PLGF is an effective image descriptor, and it outperforms other widely used existing descriptors. Even if in complicated variations like noise, and illumination with smaller databases, a combination of PLGF and convolutional neural network (CNN) performs consistently better than other state-of-the-art techniques.
本文提出了一种名为局部引力模式（PLGF）的新型局部图像描述符。它受到万有引力定律的启发。PLGF是一种混合描述符，它由两个特征分量组合而成：一个是局部引力大小模式（PLGFM），另一个是局部引力角度模式（PLGFA）。PLGFM对局部引力大小进行编码，PLGFA对中心像素在局部邻域内对所有其他像素施加的局部引力角度进行编码。我们提出了一种用于引力大小编码的新型抗噪且保边的二进制模式，称为邻域到中心差分二进制模式（NCDBP）。最后，将这两个分量的直方图连接起来构建PLGF描述符。在现有的人脸识别数据库、纹理数据库和生物医学图像数据库上的实验结果表明，PLGF是一种有效的图像描述符，并且它优于其他广泛使用的现有描述符。即使在噪声、光照等复杂变化以及较小数据库的情况下，PLGF与卷积神经网络（CNN）的组合也始终比其他先进技术表现更好。
