Image Feature Vector Construction Using Interest Point Based Regions

The paper presents a new approach for content based retrieval of images. The algorithm uses information sampled from around detected corner points in the image. A corner detection approach based on line intersections has been employed using Hough transform for line detection and then finding intersecting, near intersecting or complex shaped corners. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the corner points obtained as such retain the much desired property of repeatability and hence ensure the similar pixel samples under various transformations and are robust to noise. K-means clustering algorithm is used to assign class labels to the extracted sample mean and variance of the corner regions from a random selection of training images and used for learning a Gaussian Byes classifier to classify whole training image database. Histogram of the class members in an image has been used as a feature vector. The retrieval performance and behavior of the algorithm has been tested using four different similarity measures.