Pyramid Pooling Of Convolutional Feature Maps For Image Retrieval

We propose a novel method for content based image retrieval based on the features extracted from the convolutional layers of the deep neural network architecture. Some of the popular approaches form the feature vectors from the fully connected layers of the convolutional neural networks or directly concatenate the features from the convolutional layers. However, the main problem with the use of feature vectors from fully connected layers is that the spatial information about the objects are lost. This motivated us to use the features from the convolutional layer. We incorporate a pyramid pooling based approach to form more compact and location invariant feature vectors. We have measured the Mean Average Precision (MAP) on benchmark databases such as the Holidays and Oxford5K datasets using features extracted from the AlexNet model. The proposed method gives better retrieval results compared to other state-of-the-art approaches which use feature vectors from fully connected layers and convolutional layers without spatial pooling.