Evaluation of GIST descriptors for web-scale image search

Matthijs Douze 1 Hervé Jégou 1 Sandhawalia Harsimrat 1 Laurent Amsaleg 2 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The GIST descriptor has recently received increasing attention in the context of scene recognition. In this paper we evaluate the search accuracy and complexity of the global GIST descriptor for two applications, for which a local description is usually preferred: same location/object recognition and copy detection. We identify the cases in which a global description can reasonably be used. The comparison is performed against a state-of-the-art bag-of-features representation. To evaluate the impact of GIST's spatial grid, we compare GIST with a bag-of-features restricted to the same spatial grid as in GIST. Finally, we propose an indexing strategy for global descriptors that optimizes the trade-off between memory usage and precision. Our scheme provides a reasonable accuracy in some widespread application cases together with very high efficiency: In our experiments, querying an image database of 110 million images takes 0.18 second per image on a single machine. For common copyright attacks, this efficiency is obtained without noticeably sacrificing the search accuracy compared with state-of-the-art approaches.
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Matthijs Douze, Hervé Jégou, Sandhawalia Harsimrat, Laurent Amsaleg, Cordelia Schmid. Evaluation of GIST descriptors for web-scale image search. CIVR 2009 - International Conference on Image and Video Retrieval, Jul 2009, Santorini, Greece. pp.19:1-8, ⟨10.1145/1646396.1646421⟩. ⟨inria-00394212⟩

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