Ant colony optimization (ACO) is a population-based metaheuristic for solving hard combinatorial optimization problems. Many studies are dedicated to accelerating ACO by parallel hardware, especially by graphics processing units (GPUs). However, due to the irregular (random) pattern of ACO algorithms in data access and control flow, the performance of GPU-based approaches is constrained by hardware limitations. CPU-based SIMD computing for ACOs is rarely investigated in previous literatures, and how well multicore-SIMD CPU-based parallel ACOs could perform remains unknown. In this paper, we present and evaluate a model of vector parallel ACO for multi-core SIMD CPU architecture. In the proposed model, each ant is mapped with a CPU core and the tour construction of each ant is accelerated by vector instructions. Furthermore, based on the model, we propose a new fitness proportionate selection approach named Vector-based Roulette Wheel (VRW) in the tour construction stage. In this approach, the fitness values are grouped into SIMD lanes and the prefix sum is computed in vector-parallel mode. The proposed algorithm is tested on standard TSP instances ranging from 198 to 4461 cities and shows a speedup factor of 57.8x compared to the single-threaded CPU counterpart. More significantly, we compare our approach with high performance GPU-based ACOs, and the results demonstrate the strong potential of CPU-based parallel ACOs.

One key issue in content‐based image retrieval is to extract effective features so as to represent the visual content of an image. In this study, a new non‐negative sparse feature learning approach to produce a holistic image representation based on low‐level local features is presented. Specifically, a modified spectral clustering method is introduced to learn a non‐negative visual dictionary from local features of training images. A non‐negative sparse feature encoding method termed non‐negative locality‐constrained linear coding (NNLLC) is proposed to improve the popular locality‐constrained linear coding method so as to obtain more meaningful and interpretable sparse codes for feature representation. Moreover, a new feature pooling strategy named kMaxSum pooling is proposed to alleviate the information loss of the sum pooling or max pooling strategy, which produces a more effective holistic image representation and can be viewed as a generalisation of the sum and max pooling strategies. The retrieval results carried out on two public image databases demonstrate the effectiveness of the proposed approach.

We propose a novel robust method to accurately align large-photometric-variation and noisy images emerged from high dynamic range (HDR) imaging. First, the keypoints are detected from optimal multi-binary images to eliminate the noise and photometric effects. The feature descriptor, which is translation-, rotation- and scale-invariant and robust to noise and photometric changes, is then developed, and feature matching is implemented efficiently by use of the structure information of the keypoints. Finally, mutual information (MI) is employed in the RANSAC method for homography estimation and performance assessment, which makes the results accurate and stable. Experiments carried out on synthesized images demonstrate that the proposed method is much more robust to both photometric changes and noise than the SIFT method, the state-of-the-art alignment method.