We are concerned by the use of factorial correspondence analysis (FCA) for image retrieval. FCA is designed for analysing contingency tables. In textual data analysis (TDA), FCA analyses a contingency table crossing terms/words and documents. For adapting FCA on images, we first define rdquovisual wordsrdquo 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 rdquovisual wordsrdquo as terms/words and images as documents. In spite of its successful applications in information retrieval, FCA suffers from large dimension problem because of the diagonalization of a large matrix. We propose a new algorithm, CABoost, which overcomes this large dimension problem of FCA. The data are sampled by column (word) and a FCA is applied on the sample. After some samplings, we finally combine separated results by a weighting - principle component analysis (PCA). The numerical experiments show that our algorithm performs more rapidly than the classical FCA without losing precision.
['Nguyen-Khang Pham', 'Annie Morin', 'Patrick Gros']
Boosting of factorial correspondence analysis for image retrieval