Factorial Correspondence Analysis for Image Retrieval

Abstract—We are concerned by the use of Factorial Correspondence Analysis (FCA) for image retrieval. FCA is designed for analyzing 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 “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 [20] or Probabilistic Latent Semantic Analysis (PLSA). To scale up and improve the quality of research, we propose a new retrieval schema using inverted files based on the relevant indicators of Correspondence Analysis (the quality of representation and contribution to inertia). The numerical experiments show that our algorithm performs more rapidly than the exhaustive method without losing precision