CBIR using features derived by Deep Learning
This addresses the challenge of reducing the semantic gap in CBIR for applications like image search, though it is incremental as it applies existing deep learning methods to this domain.
The paper tackles the problem of retrieving similar images from large databases in Content-Based Image Retrieval (CBIR) by using features from pre-trained deep learning networks, resulting in vastly superior performance compared to contemporary systems and reduced retrieval times through pre-clustering.
In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the semantic gap. In this paper, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases.