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binary signatures

A binary signature is a compact sequence of bits generated by mapping a high-dimensional feature descriptor into a low-dimensional binary vector to enable rapid similarity comparison. In computer vision and image retrieval, binary signatures are used to refine feature matching beyond standard visual word quantization by encoding the precise location of a descriptor within its assigned cluster. Generated through projection and thresholding techniques, such as Hamming embedding or locality-sensitive hashing, these signatures preserve distance relationships between descriptors while enabling extremely fast distance calculations using bitwise Hamming distance operations, thereby improving the accuracy and scalability of large-scale visual search systems.

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Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

Hervé Jégou, Matthijs Douze, Cordelia Schmid

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Why you should read this

Presents Hamming embedding and weak geometric consistency to refine visual-word descriptor matching and filter geometrically inconsistent features directly within an inverted file, substantially increasing retrieval accuracy on million-scale image databases.

This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and 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 the inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.

Added

2026-09-16