66 research outputs found
Note: An object detection method for active camera
To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H
An EMO Joint Pruning with Multiple Sub-networks: Fast and Effect
The network pruning algorithm based on evolutionary multi-objective (EMO) can
balance the pruning rate and performance of the network. However, its
population-based nature often suffers from the complex pruning optimization
space and the highly resource-consuming pruning structure verification process,
which limits its application. To this end, this paper proposes an EMO joint
pruning with multiple sub-networks (EMO-PMS) to reduce space complexity and
resource consumption. First, a divide-and-conquer EMO network pruning framework
is proposed, which decomposes the complex EMO pruning task on the whole network
into easier sub-tasks on multiple sub-networks. On the one hand, this
decomposition reduces the pruning optimization space and decreases the
optimization difficulty; on the other hand, the smaller network structure
converges faster, so the computational resource consumption of the proposed
algorithm is lower. Secondly, a sub-network training method based on
cross-network constraints is designed so that the sub-network can process the
features generated by the previous one through feature constraints. This method
allows sub-networks optimized independently to collaborate better and improves
the overall performance of the pruned network. Finally, a multiple sub-networks
joint pruning method based on EMO is proposed. For one thing, it can accurately
measure the feature processing capability of the sub-networks with the
pre-trained feature selector. For another, it can combine multi-objective
pruning results on multiple sub-networks through global performance impairment
ranking to design a joint pruning scheme. The proposed algorithm is validated
on three datasets with different challenging. Compared with fifteen advanced
pruning algorithms, the experiment results exhibit the effectiveness and
efficiency of the proposed algorithm
Community mining using three closely joint techniques based on community mutual membership and refinement strategy
Community structure has become one of the central studies of the topological structure of complex networks in the past decades. Although many advanced approaches have been proposed to identify community structure, those state-of-the-art methods still lack efficiency in terms of a balance between stability, accuracy and computation time. Here, we propose an algorithm with different stages, called TJA-net, to efficiently identify communities in a large network with a good balance between accuracy, stability and computation time. First, we propose an initial labeling algorithm, called ILPA, combining K-nearest neighbor (KNN) and label propagation algorithm (LPA). To produce a number of sub-communities automatically, ILPA iteratively labels a node in a network using the labels of its adjacent nodes and their index of closeness. Next, we merge sub-communities using the mutual membership of two communities. Finally, a refinement strategy is designed for modifying the label of the wrongly clustered nodes at boundaries. In our approach, we propose and use modularity density as the objective function rather than the commonly used modularity. This can deal with the issue of the resolution limit for different network structures enhancing the result precision. We present a series of experiments with artificial and real data set and compare the results obtained by our proposed algorithm with the ones obtained by the state-of-the-art algorithms, which shows the effectiveness of our proposed approach. The experimental results on large-scale artificial networks and real networks illustrate the superiority of our algorithm
A self-paced learning algorithm for change detection in synthetic aperture radar images
Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and
has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness
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