At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

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Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification. The objective function of LC-KSVD is consisted of reconstruction error, classification error and discriminative sparse codes error with 0 -norm sparse regularization term. The 0 -norm, however, leads to NP-hard problem. Despite some methods such as orthogonal matching pursuit can help solve this problem to some extent, it is quite difficult to find the optimum sparse solution. To overcome this limitation, we propose a label embedded dictionary learning (LEDL) method to utilise the 1 -norm as the sparse regularization term so that we can avoid the hard-to-optimize problem by solving the convex optimization problem. Alternating direction method of multipliers and blockwise coordinate descent algorithm are then exploited to optimize the corresponding objective function. Extensive experimental results on six benchmark datasets illustrate that the proposed algorithm has achieved superior performance compared to some conventional classification algorithms. Figure 1:The scheme of LEDL is on the right while the LC-KSVD is on the left. The difference between the two methods is the sparse regularization term which LEDL use the 1 -norm regularization term and LC-KSVD use the 0 -norm regularization term. Compared with 0 -norm, the sparsity constraint factor of 1 -norm is unfixed so that the basis vectors can be selected freely for linear fitting. Thus, our proposed LEDL method can get smaller errors than LC-KSVD.

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Developers of parallel programming are faced with choices of using various Java locks. Choosing the best lock is a challenging task because a multithreaded application implemented using different locks may end up with uncertain performance. There is a strong need for automated tool support that helps Java programmers choose the best lock for each specific application. This paper presents an automated transformation approach to convert an implementation using a synchronized lock to a ReentrantLock or a ReentrantReadWriteLock at the bytecode level. For the ReentrantLock, our approach runs analysis for global monitors using the visitor pattern in the Joeq compiler. For the ReentrantReadWriteLock, a read or write lock is chosen after a side-effect analysis. Then, the proposed system validates the consistency of the analysis results to ensure a correct sequence of lock usage. Finally, lock operations and related thread communication operations are transformed into a ReentrantLock or a ReentrantReadWriteLock using a bytecode transformation framework Javassist. We validate our approach on several benchmarks including RBTree, PC, SPECjbb2005, and HSQLDB. The experimental results show that the proposed automated transformation approach can transform these benchmarks successfully in a timely fashion.