基于LDB描述子和局部空间结构匹配的快速场景辨识
Fast scene recognition based on LDB descriptor and local spatial structure matching

提出一种新的基于局部差异二值(local difference binary, LDB)描述子和局部空间结构匹配方法实现快速场景辨识,运用多重网格密集采样得到图像区域的灰度和梯度信息,比较网格间的灰度和梯度进行二值描述,继承了二值特征提取的快速和低存储的优点。通过构建特征点的局部空间分布约束,将局域内的多点匹配取代单点匹配,排除了大量错配点,提升了匹配的准确率。试验表明,本研究方法计算效率约是尺度不变特征变换(scale-invariant feature transform, SIFT)的2.7倍,加速稳健特征(speeded up robust features, SURF)的1.9倍,充分验证了本研究方法的有效性和识别性能。
A new local difference binary (LDB) descriptor and local spatial structure matching method was proposed to implement fast scene recognition. The multi-grid dense sampling method was used to obtain grayscale and gradient information of the image area, and the binary description was performed by comparing the grayscale and gradient size between the grids, which inherited the advantages of fast and low storage of binary feature extraction. The multi-point matching was adopted to replace the original single point of matching technology, which removed a large number of mismatches, thus the match accuracy was improved. The experiment showed that the efficiency of this method was about 2.7 times of SIFT and 1.9 times of SURF. The validity and recognition performance of the method were fully verified.