关键词: 基于内容的图像检索（CBIR）, 卷积神经网络（CNN）, 双线性模型, 低维度图像表示, 曼哈顿距离

Abstract: For the issue of the large image feature dimension in existing Content-Based Image Retrieval（CBIR） methods, a CBIR method based on improved Convolutional Neural Network（CNN） and bilinear model is proposed. Firstly, a low-dimensional pooling method is used to replace the pooling process in the traditional CNN to reduce the dimension of the image features. Then, two feature extractors are used for feature extraction based on the idea of bilinear model, and these two features are inner product at each image location to form the final image descriptor. Finally, the similarity is evaluated by calculating the Manhattan distance between images, so as to obtain the related images and their sorting. Experimental results show that the proposed method can accurately retrieve the relevant images, and has lower retrieval time and memory consumption.

Key words: Content-Based Image Retrieval（CBIR）, Convolutional Neural Network（CNN）, bilinear model, low-dimensional image representation, Manhattan distance