| dc.description.abstract | To search images from large image databases, image retrieval is the major technique to retrieve similar images based on users’ queries. In order to allow users to provide keyword-based queries, automatically annotating images with keywords has been extensively studied. In particular, BOW(Bag Of Words)and SPM(Spatial Pyramid Matching Bag Of Words) are two well-known methods to represent image content as the image feature descriptors. To extract the BOW or SPM features, some keypoints must be detected from each image. However, the number of the detected keypoints is usually very large and some of them are unhelpful to describe the image content, such as background and similar keypoints in different classes. In addition, the computational cost of the vector quantization step heavily depends on the amount of detected keypoints.
Therefore, in this thesis I introduce a new algorithm called IKS(Iterative Keypoint Selection), whose aim is to select representative keypoints for generating the BOW and SPM features. The main concept of IKS is based on identifying some representative keypoints and the distance to select useful keypoints. Specifically, IKS can be divided into IKS1 and IKS2 according to the strategy of identifying representative keypoints. While IKS1 focuses on randomly selecting a keypoint from an image as the representative keypoint, IKS2 uses the k-means to generate the cluster centroids to find the representative keypoints that is closest to them.
Our experimental results based on the Caltech101 and Caltech256 datasets demonstrate that performing keypoint selection by IKS1 and IKS2 can allow the SVM classifier to provide better classification accuracy than the baseline BOW and SPM without keypoint selection. More specifically, IKS2 is more appropriate than IKS1 for image annotation since it performs better than IKS1 when the larger dataset, i.e. Caltech 256, is used.
| en_US |