Color constancy is the ability to measure colors of objects independent of the light source color. Various methods have been proposed to handle this problem. Most of them depend on the statistical distributions of the pixel values. Recent studies show that incorporation image derivatives are more effective than the direct use of pixel values. Based on this idea, a novel edge-based color constancy algorithm using support vector regression (SVR) is proposed. Contrary to existing SVR color constancy algorithm, which is computed from the zero-order structure of images, our method is based on the higher-order structure of images. The experimental results show that our algorithm is more effective than the zero-order SVR color constancy methods.

Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.

This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.

In this letter, we use the saliency maps obtained by several bottom-up methods to learn a model to generate a bottom-up saliency map. In order to consider top-down image semantics, we use the high-level features of objectness and background probability to learn a top-down saliency map. The bottom-up map and top-down map are combined through a two-layer structure. Quantitative experiments demonstrate that the proposed method and features are effective to predict human fixation.

We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.

To further suppress the multiple-access interference (MAI) in frequency-hopping multiple-access (FHMA) system, a novel kind of FH sequence set named as no-hit-zone (NHZ) sequence, is proposed for an FHMA system with M-ary FSK modulation (MFSK/FHMA) in this paper. Expressions for the decision variables are derived for the asynchronous MFSK/FHMA system with NHZ sequence set (MFSK/FHMA-NHZ) under a slow Rayleigh-fading channel model. For the special case of M=2, accurate analytic bit error rate (BER) is derived as a function of maximum relative delay D and the number of users K by a characteristic function method. The theoretical results validated by Monte Carlo simulations are used to investigate the dependence of the average BER on D and K. Comparison with the MFSK/FHMA system with Markov hit pattern (MFSK/FHMA-Markov) shows that MFSK/FHMA-NHZ system performs better than Markov hit pattern system as long as D is restricted in a certain range and further, the gain in the performance increases with increase in the value of K.

Group multicasting is a generalization of multicasting whereby every member of a group is allowed to multicast messages to other members that belongs to the same group. The group multicast routing problem (GMRP) is that of finding a set of multicast trees with bandwidth requirements, each rooted at a member of the group, for multicasting messages to other members of the group. An optimal solution to GMRP is a set of trees, one for each member of the group, that incurs the least overall cost. This problem is known to be NP-complete and hence heuristic algorithms are likely to be the only viable approach for computing near optimal solutions in practice. In this paper, we derive a lower bound on the cost of an optimal solution to GMRP by using Lagrangean Relaxation and Subgradient Optimization. This lower bound is used to evaluate the two existing heuristic algorithms in terms of their ability to find close-to-optimal solutions.

In this paper we addresses the problem of finding feasible solutions for the Group Multicast Routing Problem (GMRP). This problem is a generalization of the multicast routing problem whereby every member of the group is allowed to multicast messages to other members from the same group. The routing problem involves the construction of a set of low cost multicast trees with bandwidth requirements for all the group members in the network. We first prove that the problem of finding feasible solutions to GMRP is NP-complete. Following that we propose a new heuristic algorithm for constructing feasible solutions for GMRP. Simulation results show that our proposed algorithm is able to achieve good performance in terms of its ability of finding feasible solutions whenever one exist.

As a promising future network architecture, Information-centric networking (ICN) has attracted much attention, its ubiquitous in-network caching is one of the key technologies to optimize the dissemination of information. However, considering the diversity of contents and the limitation of cache resources in the Internet, it is usually difficult to find a one-fit-all caching strategy. How to manage the ubiquitous in-network cache in ICN has become an important problem. In this paper, we explore ways to improve cache performance from the three perspectives of spatiality, temporality and availability, based on which we further propose an in-network cache management strategy to support differentiated service. We divide contents requested in the network into different levels and the selection of caching strategies depends on the content level. Furthermore, the corresponding models of utilizing cache resources in spatiality, temporality and availability are also derived for comparison and analysis. Simulation verifies that our differentiated service based cache management strategy can optimize the utilization of cache resources and get higher overall cache performance.

To assess the performance of maximum-likelihood (ML) based Nakagami m parameter estimators, current methods rely on Monte Carlo simulation. In order to enable the analytical performance evaluation of ML-based m parameter estimators, we study the statistical properties of a parameter Δ, which is defined as the log-ratio of the arithmetic mean to the geometric mean for Nakagami-m fading power. Closed-form expressions are derived for the probability density function (PDF) of Δ. It is found that for large sample size, the PDF of Δ can be well approximated by a two-parameter Gamma PDF.

Several methods have be proposed or used to optimize the phase distribution of a kinoform. In this paper, we proposed a fast algorithm for optimization of the kinoform based on simulated annealing to reduce the large computation cost. This method uses a simplified equation to calculate the energy function after perturbation.

In this paper, a new finger vein recognition method based on Gabor wavelet and Local Binary Pattern (GLBP) is proposed. In the new scheme, Gabor wavelet magnitude and Local Binary Pattern operator are combined, so the new feature vector has excellent stability. We introduce Block-based Linear Discriminant Analysis (BLDA) to reduce the dimensionality of the GLBP feature vector and enhance its discriminability at the same time. The results of an experiment show that the proposed approach has excellent performance compared to other competitive approaches in current literatures.

Subcarrier pairing (SP) and power allocation (PA) can improve the channel capacity of the OFDM multi-hop relay system. Due to limitations of processing complexity and energy consumption, symbol-level relaying, which only regenerates the constellation symbols at relay nodes, is more practical than code-level relaying that requires full decoding and encoding. By modeling multi-hop symbol-level relaying as a multi-staged parallel binary symmetric channel, this paper introduces a jointly optimal SP and PA scheme which maximizes the end to end data rate. Analytical arguments are given to reveal the structures and properties of the optimal solution, and simulation results are presented to illustrate and justify the optimality.

In this paper, we focus on the multi-way relaying channel where K users wish to exchange information with each other within two phases. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and cross-pair interference can be cancelled. Reliable detection is challenging at the relay since the observation constellation is no longer one of the regular ones, due to the fact that a relay observation is the superposition of the messages from one of the $rac{1}{2}K(K-1)$ user pairs. When the trellis coded modulation is used at each node, a simple constellation mapping function and a reduced-states decoding scheme can be applied at the relay, which result in much lower complexity. Furthermore, a modified version of the decoding method is also developed which is called the re-encoding-avoidance scheme at the relay. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed scheme.

In this letter, we propose a novel intra prediction coding scheme for H.264/AVC. Based on our proposed minimum distance prediction (MDP) scheme, the optimal reference samples for predicting the current pixel can be adaptively updated corresponding to different video contents. The experimental results show that up to 2 dB and 1 dB coding gains can be achieved with the proposed method for QCIF and CIF sequences respectively.