图书图像检索是基于内容的图像检索(CBIR)的一个重要应用。尺度不变特征变换(SIFT)与方向梯度直方图(HOG)在CBIR和物体识别等领域有着广泛应用。根据图书图像的特点,把图书图像按照九宫格进行空间划分,并以SIFT特征为基础对每个分块子图像进行特征描述,然后结合HOG特征组成联合特征描述,联合特征对图书图像的描述力较强而且数据量较小。采用"倒数比"的方式进行联合特征相似性度量能够有效防止子图像发生突变而造成的巨大误差。在规模为 50000 幅的图书图像库上的实验结果表明,检索的精度和耗时都在可接受范围内。

Books image retrieval is an important application of CBIR (content-based image retrieval). SIFT(scale inva-riant feature transform) and HOG(histogram of oriented gradient) are widely used in CBIR and object recognition. A book image is divided into nine blocks called sub-images according to its characteristics. We extract features from the image based on SIFT, and then we combine the features with the HOG feature to create a combined feature. The combined feature occupies little data and describes an image well. We use a new way to calculate the combined features’ similarity, which will avoid large errors caused by some sub-image. An image set with 50000 book images is used to test our method and we find the accuracy to be acceptable and the time consumed by the retrieval process is quite short.