BIHL:A Fast and High Performance Object Proposals based on Binarized HL Frequency
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In recent years, the use of object proposal as a preprocessing step for target detection to improve computational efficiency has become an effective method. Good object proposal methods should have high object detection recall rate and low computational cost, as well as good location accuracy and repeatability. However, it is difficult for current advanced algorithms to achieve a good balance in the above performance. Therefore, it is especially important to ensure that the recall rate and location quality are not degraded while accelerating object generation.For this problem, we propose a class-independent object proposal algorithm BIHL. It combines the advantages of window scoring and superpixel merging. First, a binarized horizontal high frequency component feature and a linear classifier are used to learn and generate a set of candidate boxs with a objective score. Then, the candidate boxs are merged based on the principle of location and score proximity. Different from superpixel merging algorithm, our method does not use pixel level operation to avoid a lot of computation without losing performance. Experimental results on the VOC2007 dataset and the VOC2007 synthetic interference dataset containing 297,120 test images show that when including difficult-to-identify objects with an IOU threshold of 0.5 and 10000 budget proposals, our method achieves a 99.3 best overlap of 81.1 set images is 0.0015 seconds, which is nearly 3 times faster than the current fastest method. In repeatability testing, our method is the method with the highest average repeatability among the methods that achieve good repeatability to various disturbances, and the average repeatability is 10 The code will be published in https://github.com/JiangChao2009/BIHL
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