Nonlinear embedding neural codes for visual instance retrieval.

The state-of-the-art visual instance retrieval systems are based on learning methods, which require collecting an external dataset and fine-tuning a convolutional neural network for the specific retrieval task. In contrast, non-learning methods just used the pre-trained network on the ImageNet classification task for feature extraction, but have trailed the accuracy of learning based methods thus far. In this paper, we propose a non-learning nonlinear embedding neural codes approach that, for the first time, outperforms the state-of-the-art more complex learning based visual instant retrieval methods. We discover that nonlinear embedding can produce a new feature space, where relevant image features are closer to each other. Compared to previous learning based approaches, we do not need to collect any extra datasets and fine-tune the convolutional neural network, but our method can deliver superior performance in the image retrieval task. In addition, we can also reduce the feature dimensionality during nonlinear embedding, but our method does not reduce the retrieval accuracy with the decrease of feature dimensions. Our experiments on three public available datasets for instance retrieval demonstrate that the proposed method achieved outstanding performance against state-of-the-art methods. Remarkably, our 8D image vector can surpass existing 256D compact representation, which demonstrates that our method can effectively improve the speed and precision of visual instance retrieval systems. [ABSTRACT FROM AUTHOR]