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.