Application research of deep learning and iterative quantization in image retrieval

The key of content-based image retrieval lies in feature extraction and multi-bit quantization coding. In recent years, content-based image retrieval uses low-level visual features to describe images, and there is a "semantic gap" problem. Besides, the traditional quantization coding uses the randomly generated projection matrix, which is independent of the feature data, so it can not guarantee the accuracy of quantization. Aiming at these problems, this paper proposes an image retrieval method based on convolution neural network VGG16 and iterative quantization (ITQ). We use the Network Model VGG16 pre-trained on an open dataset. The method of ITQ is used to train the Hashing function, the minimum quantization error between the feature and the hash code with the bits of set number continuously approximated, so the quantization error is minimized. Finally, the obtained hash code is used for image retrieval. In this paper, recall rate, precision rate and mean average precision are used as the index to evaluate the retrieval results, and the results are obtained based on the Caltech256 image database. Experimental results show that the proposed algorithm is better than other mainstream image retrieval algorithms.