图像的形状是描述图像的重要视觉和语义特征，可通过图像中像素点的区域分布表现出来。为了对二值图像进行有效检索，提出了一种基于区域的形状特征——密度分布特征,用来进行二值图像检索。该方法在经过形心定位和子图像区域划分后，可得到2个M维特征向量，其中第1个表示各个子图像区域的目标像素的相对密度，第2个表示各个子图像区域的目标像素在极坐标方向上的相对密度的一阶数值差分。在进行相似性度量时，首先采用Gaussian模型对用这2个特征向量计算得到的距离分别进行归一化处理；然后综合两个特征向量的距离计算总的相似度。实验结果表明，密度分布特征不仅能够有效地刻画二值图像的形状，具有非常好的平移、尺度和旋转不变性，而且检索结果优于Hu不变矩。

Shape is a very important visual and semantic feature used to depict image, and it can be revealed by image pixels’ regional distribution. This paper proposes a region-based shape representation, a new “density distribution feature (DDF)”. After shape center orientation and region partition, two M dimensional feature vectors are got. The first feature vector represents the relatively density of object pixels within each sub-image. And the second represents the difference of relatively density in the direction of radial coordinates. When matching the similarity, we first used the Gaussian model to normalize the two dimensional feature vectors. Then we integrated them to calculate similarity distance. The experiments results showed that this shape feature can depict the image well and is invariant to translation, scale and rotation. The paper also evaluated the effectiveness of the proposed descriptor with respect to Moment Invariants.