Image Recognition Technique using Local Characteristics of Sub-sampled Images

An image recognition technique utilizing a database of image characteristics is introduced. This technique is different from eigenimage method which requires a large amount of information of training set images in terms of the size of each image and the number of images in the database. Especially, this technique is useful for recognizing images which have fixed shape and structure such as paintings and documents. In this study, images of 33 different classic paintings taken by a common camera phone are used to construct the database and a MATLAB code is written for image recognition. 66 different images of the 33 paintings are tested and approximately 80 % of them are recognized correctly. In the code, low pass filters for noise reduction, morphological operators such as dilation and majority filter for clear and smooth boundaries and Haralick corner detector to find characteristic points are used. To construct a database which consists of small size images, original images are trimmed to be a smaller image which contains only the region of interest. Furthermore, each image is sub-sampled to be a fixed size gray image which is 200 pixels by 200 pixels. Sub-sampling can reduce discrepancy of trimming position and angle/position of camera. Finally, using Haralick corner detector, 100 corner points which have large cornerness are selected per image. The points are positioned on a 200 by 200 binary image, which is a reference image in database. Some conventional image processing techniques are applied to an input image. The resulting image is also converted to a binary 200 pixels by 200 pixels image and compared with the 33 reference images in the database being shifted and warped.