Multiresolution scalable image and video segmentation

Due to the popularity of multimedia applications, many efforts have been directed towards presenting new services and functionalities such as interactivity, manipulation, content-based retrieval, scalability, etc. Object-based image/video representation and processing is one of the approaches considered to meet these desired functionalities. However, semantic image and video segmentation is one of the unresolved challenges of this approach. Although many works on segmentation have contributed towards this goal, there are still numerous areas requiring further research. In this work, a comprehensive range of image and video segmentation algorithms, including low and high level phases, are proposed, tested and analysed. In the low level phase, the image/frame is partitioned into homogeneous regions while in the high level phase , the “objects-of-interest” are extracted. The proposed algorithms are useful for generic segmentation applications, in particular for scalable coding, which distributes information over heterogeneous networks. One of the requirements of the scalable coding is that the shapes of an object produced at different resolutions should be similar, more precisely, the low resolution objects should be the down sampled version of the higher resolution objects. A multidimensional processing integrated with the multiresolution segmentation processing reduces computational complexity and provides a scalability feature for the extracted objects/regions at different resolutions, which is necessary for the scalable coding algorithms. Including smoothness as a visual quality criterion in the segmentation and classification algorithms improves the visual effect of the segmentation results. To meet the scalability and smoothness constraints, a Markov Random Field (MRF) framework with enough flexibility to meet the constraints is utilised. The proposed