Automatic similarity learning using SOTM for CBIR of the WT/VQ coded images

The unsupervised learning network is explored to incorporate self-learning capability into image retrieval systems. More specifically, we propose the adoption of a self organizing tree map (SOTM) to implement a self-learning methodology that allows minimization of the role of users in an effort to automate interactive retrieval. This automatic-learning mode is applied to interactive retrieval strategies such as the radial basis function method and the relevance feedback method. The proposed method has been applied to retrieve the images compressed by wavelet transform and vector quantization coders. Retrieval performances are compared with conventional retrieval systems employing both non-interactive and user controlled interactive retrieval using the MIT texture database. The results obtained are compared favorably with preceding methods