Cognitive bridge between haptic impressions and texture images for subjective image retrieval

As a step towards subjective image retrieval, This work reports an on-going collaboration project between Waseda University and SUNY, Binghamton, on relating texture images to haptic impressions. To capture the surface height variations, texture images are taken under different illuminations and viewing conditions. Our method applies a new frequency analysis method to the texture images. We evaluate the performances of our feature and other typical conventional features by checking whether texture images are correctly classified into "soft" or "hard" by the SVM (support vector machine) method, where the training data for the SVM are collected by subjective tests. Experimental results show that our texture feature can classify "soft" or "hard" better than the other features.