基于多中心卷积特征加权的图像检索方法

Multi-center convolutional feature weighting based image retrieval

深度卷积特征能够为图像内容描述提供丰富的语义信息,为了在图像表示中突出对象内容,结合激活映射中较大响应值与对象区域的关系,提出基于多中心卷积特征加权的图像表示方法。首先,通过预训练深度模型提取出图像卷积特征;其次,通过不同通道特征映射求和得到激活映射,并将激活映射中有较大响应值的位置认为是对象的中心;再次,将中心数量作为尺度,结合激活映射中不同位置与中心的距离为对应位置的描述子加权;最后,合并不同中心数量下的图像特征,生成图像表示用于图像检索。与池化卷积(SPo C)算法和跨维度(Cro W)算法相比,所提方法能够为图像表示提供尺度信息的同时突出对象内容,并在Holiday、Oxford和Paris图像集中取得了良好的检索结果。

Deep convolutional features can provide rich semantic information for image content description.In order to highlight the object content in the image representation,the multi-center convolutional feature weighting method was proposed based on the relationship between high response positions and object regions.Firstly,the pre-trained deep network model was used to extract the deep convolutional features.Secondly,the activation map was obtained by summing the feature maps in all the channels and the positions with top few highest responses were considered as the centers of the object.Thirdly,the number of the centers was considered as the scale,and the descriptors corresponding to different positions were weighted based on the distances between these centers and the positions.Finally,the image representation for image retrieval was generated by merging the image features obtained based on different numbers of centers.Compared with Sum-pooled Convolutional(SPoC)algorithm and Cross-dimensional(CroW)algorithm,the proposed method can provide scale information and highlight the object content in the image representation,and achieves excellent retrieval results in the Holiday,Oxford and Paris image retrieval datasets.