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Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening

Hervé Jégou 1 Ondrej Chum 2 
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The paper addresses large scale image retrieval with short vector representations. We study dimensionality reduction by Principal Component Analysis (PCA) and propose improvements to its different phases. We show and explicitly exploit relations between i) mean subtraction and the negative evidence, i.e., a visual word that is mutually missing in two descriptions being compared, and ii) the axis de-correlation and the co-occurrences phenomenon. Finally, we propose an effective way to alleviate the quantization artifacts through a joint dimensionality reduction of multiple vocabularies. The proposed techniques are simple, yet significantly and consistently improve over the state of the art on compact image representations. Complementary experiments in image classification show that the methods are generally applicable.
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Submitted on : Thursday, August 2, 2012 - 4:39:09 PM
Last modification on : Thursday, January 20, 2022 - 4:18:13 PM
Long-term archiving on: : Friday, December 16, 2016 - 4:44:01 AM


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  • HAL Id : hal-00722622, version 2


Hervé Jégou, Ondrej Chum. Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening. ECCV - European Conference on Computer Vision, Oct 2012, Firenze, Italy. ⟨hal-00722622v2⟩



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