Abstract: Image representations derived from pre-trained convolutional neural networks (CNNs) have become the new state of the art in the task of image retrieval. But these methods are all based on image global representations and can’t be applied to the retrieval of query objects which only occupy the part area of the retrieved images. To solve these problems, this work explores the suitability for object retrieval of small query objects which only occupy part area of the retrieved images using pre-trained fully convolutional networks. First, we take advantage of the fully convolutional networks without the restriction of the size of input image，and given retrieved images，feature matrix representations are derived by fully convolutional networks. Second, given the query object, the feature can also be derived by the fully convolutional networks. Finally, the feature of query object is matched with each feature of the feature matrix of the retrieved image, and the similarity and optimal matching location are obtained. We further investigate the suitability of the multi-scale, multi-ratio transformation for different sizes of query object in the retrieved image. Experimental results on the benchmark dataset Oxford5K show that our method outperforms other state-of-the-art methods. We further demonstrate its scalability and efficacy on the Logo dataset which is collected randomly from the Internet.