Large Scale Deep Convolutional Neural Network Features Search with Lucene
This work addresses the challenge of scalable image retrieval for users needing combined textual and visual search, though it is incremental as it adapts existing methods to new data.
The paper tackled the problem of efficient content-based retrieval on large image databases by converting deep convolutional neural network features into a textual form for indexing with Lucene, resulting in a system that handles about 100 million images with optimized index occupation and query response time.
In this work, we propose an approach to index Deep Convolutional Neural Network Features to support efficient content-based retrieval on large image databases. To this aim, we have converted the these features into a textual form, to index them into an inverted index by means of Lucene. In this way, we were able to set up a robust retrieval system that combines full-text search with content-based image retrieval capabilities. We evaluated different strategies of textual representation in order to optimize the index occupation and the query response time. In order to show that our approach is able to handle large datasets, we have developed a web-based prototype that provides an interface for combined textual and visual searching into a dataset of about 100 million of images.