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Use of relevant indicators of Correspondence Analysis to improve image retrieval

Khang-Nguyen Pham 1 Quyet-Thang Le 1 Annie Morin 2 Patrick Gros 2, 3 
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We are concerned by the use of Factorial Correspondence Analysis (FCA) for image retrieval. FCA is designed for analyzing contingency tables. For adapting FCA on images, we first define "visual words" computed from Scalable Invariant Feature Transform (SIFT) descriptors in images and use them for image quantization. At this step, we can build a contingency table crossing "visual words" as terms/words and images as documents. The method was tested on the Caltech4 and Stewénius and Nistér datasets on which it provides better results (quality of results and execution time) than classical methods as tf*idf or Probabilistic Latent Semantic Analysis (PLSA). To scale up and improve the retrieval quality, we propose a new retrieval schema using inverted files based on the relevant indicators of Correspondence Analysis (quality of representation and contribution to inertia). The numerical experiments show that our algorithm performs faster than the exhaustive method without losing precision.
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Submitted on : Monday, July 15, 2013 - 7:03:56 PM
Last modification on : Thursday, January 20, 2022 - 4:18:35 PM


  • HAL Id : hal-00844769, version 1


Khang-Nguyen Pham, Quyet-Thang Le, Annie Morin, Patrick Gros. Use of relevant indicators of Correspondence Analysis to improve image retrieval. ITI - 32nd International Conference on Information Technology Interfaces, Jun 2010, Dubrovnik, Croatia. pp.591-596. ⟨hal-00844769⟩



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