Extracting meaningful regions for content-based retrieval of image and video

摘要

Meaningful region is the intermediate level between the original image and the interesting object of image. This level is an effective visual level for the representation of images and the successful extraction of meaningful regions from images helps to perform semantic segmentation. This paper proposes a scheme for roughly extracting meaningful regions in an image. By using multi-dimensional low-level feature analysis the local level of reliability of different features can be determined in order to adaptively weight the contribution of each feature to the segmentation process. Since the large variance of one feature always indicates that this feature would distinguish different objects clearly, a new weighted non-parametric clustering algorithm in the density space is implemented with suitably decided weights for different features. This permits us to utilize all the features efficiently and to extract semantic meaning from images. The above technique is proposed along with a retrieval application of landscape images. In this application, the object recognition plays an important role. The meaningful regions extracted should be merged into objects and more subtly semantic meaning could be obtained. Experiments on extracting meaningful regions both from still images and video clips are carried out with some satisfactory results.