Large Scale Deep Convolutional Neural Network Features Search With Lucene

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keygennaro2016large

Related papers

• A Fast Content-based Image Retrieval Method Using Deep Visual Features (2019)6.77
• Sketches Image Analysis: Web Image Search Engine Usinglsh Index And DNN Inceptionv3 (2021)0.00
• CBIR Using Features Derived By Deep Learning (2020)12.02
• Co-occurrence Of Deep Convolutional Features For Image Search (2020)9.76
• From Selective Deep Convolutional Features To Compact Binary Representations For Image Retrieval (2018)10.35
• Exploiting Local Indexing And Deep Feature Confidence Scores For Fast Image-to-video Search (2018)2.26
• A Decade Survey Of Content Based Image Retrieval Using Deep Learning (2020)18.33
• Large-scale Image Retrieval With Attentive Deep Local Features (2016)30.63