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In this paper, we propose a new deep artificial neural network architecture for synthetic aperture radar target recognition codenamed DeepSAR-Net. Unlike most existing methods, this approach can learn discriminative features directly from the training data instead of requiring pre-specification or pre-selection by a human designer. Furthermore, our method is adaptable, and it is learning to recognize...
In this paper, the latent variable model is adopted to re-describe MI-SVM and its feature mapping variants. MI-SVM with latent variable description and the corresponding stochastic optimization learning algorithm are proposed. In the Musk and Corel datasets, the proposed algorithm achieves higher predicting accuracy and faster learning speed, with strong stability and robustness for parameters and...
In this paper, we propose a novel multi-label image annotation for image retrieval based on annotated keywords. For multi-label image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions with image segmentation algorithm, pre-trained...
Automatic semantic annotation of video events has received a large attention from the scientific community in the latest years. Events can be defined by spatio-temporal relations and properties of objects and entities, which change over time; some events can be described by a set of patterns. Despite this application of dynamic graphical modeling, the performance for event modeling and detection continues...
In this paper, we propose a novel framework of large-scale and real-time image annotation system. The large-scale image set is constructed based on current Web image search engines and re-ranking algorithm. Various global and local features are employed for representing images with parallel extraction mechanism for the real-time requirements. At training stage, the distance between class centers in...
Automatic image annotation is very important for image retrieval. Despite continuous efforts in inventing new annotation algorithms, the annotation performance is usually unsatisfactory, and the annotation vocabulary is still limited due to the use of a small scale training set. In this paper, a novel image automatic annotation system based on the WordNet is presented, named WordNet-based image annotation...
Multimedia content description interface (MPEG-7) includes a number of image feature descriptors to represent low-level image features such as colors, textures and shapes effectively. But, the contribution of each descriptor may not be the same for a domain specific image database when computing the similarity measure. Machine learning techniques for the optimization of feature descriptor weights...
In image annotation system, performance is highly desired. We use a bi-coded chromosome-based genetic algorithm to optimize the weights of multimedia content description interface (MPEG-7) feature descriptors and select optimal feature descriptor subset simultaneously. Two genetic codes are used: a real code represents the weights corresponding to MPEG-7 descriptors; a binary one denotes the presence...
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