Salient objects detection aims to locate objects that capture human attention within images. Recent progresses in saliency detection have exploited the center prior, to combine with other cues such as background information, object size or region contrast, achieving competitive results. However, previous approaches of center prior supposing salient object locates nearly at image center is very ...

SUMMARY Affinity propagation (AP) clustering has recently gained increasing popularity in bioinformatics. AP clustering has the advantage that it allows for determining typical cluster members, the so-called exemplars. We provide an R implementation of this promising new clustering technique to account for the ubiquity of R in bioinformatics. This article introduces the package and presents an ...

In this task, we build fast video indexing systems using a kind of efficient features based on the entropy of pixel projections. These features of 45 dimensions, called Profil Entropy Features (PEF), are derived using the projection in the horizontal orientation. These features are then fed to SVMs to produce the keyframe ranks, from which we can get the shot ranks. In the runs, we divided the ...

More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to ...

Cluster analysis partitions a dataset into a reasonable number of disjoint groups, where each group contains similar patterns. Due to a high number of spectral channels hyper Remote Sensing are difficult to classify with high accuracy and efficiency. In this paper we propose a new image clustering method MD-AP( Manhattan Distance Based Affinity Propagation) for extract the Land cover Classifica...

Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for l...

Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm kno...

Serpentine G-protein-coupled cAMP receptors are key components in the detection and relay of the extracellular cAMP waves that control chemotactic cell movement during Dictyostelium development. During development the cells sequentially express four closely related cAMP receptors of decreasing affinity. In this study, we investigated the effect of cAMP receptor type and affinity on the dynamics...

To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operati...

Theorem 1 (i.e., Theorem 3 in the paper) shows that the stability of a linear propagation model can 8 be maintained by regularizing all the weights of each pixel in the hidden layer such the summation 9 of their absolute values is less than one. For the one-way connection, Chen et al. [1] maintain each 10 scalar output p to be within (0, 1). Liu et al. [4] extend the range to (−1, 1), where the...