Image-to-Image Retrieval Using Computationally Learned Bases and Color Information

New methods for joint compression and Image-to-image retrieval (12I retrieval) are presented. The novelty exists in the usage of computationally learned image bases besides color distributions. The bases are obtained by the Principal Component Analysis and/or the Independent Component Analysis. On the image compression, PCA and ICA outperform the JPEG's DCT This superiority holds even if the bases and superposition coefficients are quantized and encoded. On the 12I retrieval, the precision-recall curve is used to measure the performance. It is found that adding the basis information always increases the baseline ability of the color information. Besides the retrieval evaluation, a unified image format called RIM (Retrieval-aware IMage format) for effective packing of codewords including bases is specified. Furthermore, an image search viewer called Wisvi (Waseda Image Search Viewer) is developed and exploited. A beta-version of all source codes can be down-loaded from a web site given in the text.