Improving Bag-of-Features for Large Scale Image Search

This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all images in the dataset. We then introduce a graph-structured quantizer which significantly speeds up the assignment of the descriptors to visual words. A comparison with the state of the art shows the interest of our approach when high accuracy is needed. Experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric co...

• Type: Journal
• Year: 2010
• Where: IJCV
• Authors: Herve Jegou, Matthijs Douze, Cordelia Schmid
• Keywords: Geometric Consistency Constraints, IJCV 2010, Images, Weak Geometric Consistency