中文摘要：

针对传统的不变量提取方法耗时巨大、对图像背景和前景目标不加取舍、特征冗余多而且区分性差的问题,提出一种目标级签名提取方法——BLIP.首先提出一种快速的仿射不变局部特征检测与描述方法,将提取速度由秒级提升到毫秒级;然后采用改进的二进制化赋范梯度算法检测图像中的前景目标,并将目标区域内BLIP的空间上下文编码为二进制图像签名.实验结果表明,BLIP方法对网络中常见的拷贝攻击具有良好的鲁棒性,提取时间仅需8.9 ms,消耗的内存分别为尺度不变特征变换算法的0.17%,二进制鲁棒尺度不变关键点算法的2%.

英文摘要：

The problems of conventional ‘visual invariant＇ detection methods were high computational complexity, indiscriminate processing in foreground and background regions, and generating redundant and low distinctive features. An object level image signature called BLIP is proposed to deal with these problems. At first, we develop efficient affine-invariant local detector and descriptor, speeding up the extraction from second magnitude to millisecond. Next, improved BING is used to detect foreground object regions, and the context of BLIP is encoded into binary signatures. Experiments demonstrate BLIP is robust to common copy transformations in Internet. The signature can be extracted in 8.9 ms and only consumes 0.17%, 2% memory cost of SIFT and BRISK separately.