This letter presents a hyperspectral image classification method based on relevance vector machines (RVMs). Support vector machine (SVM)-based approaches have been recently proposed for hyperspectral image classification and have raised important interest. In this letter, it is genuinely proposed to use an RVM-based approach for the classification of hyperspectral images. It is shown that approximately the same classification accuracy is obtained using RVM-based classification, with a significantly smaller relevance vector rate and, therefore, much faster testing time, compared with SVM-based classification. This feature makes the RVM-based hyperspectral classification approach more suitable for applications that require low complexity and, possibly, real-time classification.
One-bit transform (1BT)-and two-bit transform (2BT)-based block motion estimation (ME) schemes have been proposed in the literature to reduce the computational complexity of the ME process by enabling simple Boolean EX-OR matching of lower bit depth representations of image frames. Recently a multiplication-free 1BT (MF-1BT) has been proposed to facilitate 1BT to be carried out with integer arithmetic using addition and shifts only. Thresholding schemes are typically used in order to construct the lower bit depth representations utilized in 1BT and 2BT. In our experience we have observed that one problem with such schemes is that pixel values that lie on directly opposite sides of the threshold are categorized into separate classes and are, therefore, counted as a nonmatch in the search process even if they are close in value. A constrained 1BT (C-1BT) that restricts pixels with values adjacent to the transform threshold during 1BT matching, counting them as a match regardless of their 1BT value, is proposed in this paper. It is shown that the proposed C-1BT approach improves the ME accuracy of 1BT-based ME and even outperforms 2BT-based ME at macroblock level.Index Terms-Image/video coding and transmission, motion estimation (ME), one-bit transform (1BT), two-bit transform (2BT).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.