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In this paper, an innovative method called extreme learning machine with hybrid local receptive fields (ELM-HLRF) is presented for image classification. In this method, filters generated by Gabor functions and the randomly generated convolution filters are incorporated into the convolution filter kernels of local receptive fields based extreme learning machine (ELM-LRF). Extreme learning machine (ELM)...
Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional...
As an important role of oceanographic survey, side-scan sonar image classification has attracted much attention in the past two decades. Due to the special properties of sonar image, traditional approaches are difficult to get good classification accuracy, so their implementation in real world is blocked. In this paper, a novel classification system based on kernel-based extreme learning machine (KELM)...
Fisher vector coding methods have been demonstrated to be effective for image classification. With the help of convolutional neural networks (CNN), several Fisher vector coding methods have shown state-of-the-art performance by adopting the activations of a single fully-connected layer as region features. These methods generally exploit a diagonal Gaussian mixture model (GMM) to describe the generative...
Transfer learning methods have demonstrated state-of-the-art performance on various small-scale image classification tasks. This is generally achieved by exploiting the information from an ImageNet convolution neural network (ImageNet CNN). However, the transferred CNN model is generally with high computational complexity and storage requirement. It raises the issue for real-world applications, especially...
Visual matching algorithms can be described in terms of visual content representation and similarity measure. With local feature based representations, visual matching can be restated as: 1) how to obtain visual similarity from the local kernel matrix, and 2) how to calculate the local kernel matrix effectively and efficiently. Existing methods mostly focus on the former, and use Euclidean distance...
Recently, the Bag-of-visual Words (BoW) based image representation has drawn much attention in image categorization and retrieval applications. It is known that the visual codebook construction and the related quantization methods play the important roles in BoW model. Traditionally, visual codebook is generated by clustering local features into groups, and the original feature is hard quantized to...
Video scene segmentation and classification are fundamental steps for multimedia retrieval, browsing and indexing. In this paper, we present a robust scene segmentation approach based on the Markov Chain Monte Carlo (MCMC) method using the structure of video sequences. In our method, there are two novel approaches to segment video sequences into scenes. The first approach is the use of the video structures...
Based on MILES algorithm, we propose a novel multiple instance learning approach which regards visual word dictionary as feature space, and combines segmentation for object detection and extraction in the process of instance classification. This approach uses "Bag of Words" model. The whole image is considered as a multiple instance bag. The visual words that represent the image are regarded...
In recent years, the Bag-of-visual Words image representation has led to many significant results in visual object recognition and categorization. However, experiments show that the unsupervised clustering of primitive visual features tends to result in the limited discriminative ability of the visual codebook, since it does not take the spatial relationship between visual primitives into consideration...
Recently, bag of words (BoW) model has led to many significant results in visual object classification. However, due to the limited descriptive and discriminative ability of visual words, the resulting performance of visual object classification is still incomparable to its analogy in text domain, i.e. document categorization. Furthermore, for weakly labeled image data, where we only know whether...
Texture classification plays an important role in image analysis. The wavelet transform is a very efficient multiscale analysis method that has been successfully applied to describe the texture. The double-density dual-tree wavelet transform can simultaneously possess the properties of the double-density discrete wavelet transform (DWT) and the dual-tree DWT. In this paper, the texture feature based...
Texture classification plays an important role in image analysis. The wavelet is a very efficient multiscale analysis method that has been successfully applied to describe the texture. However, it is translation-invariant. The recent double-density discrete wavelet transform have two interesting property, low computational complexity and nearly shift invariant. In this paper, the texture feature based...
As there is a large gap between high-level semantics and low-level features, it is difficult to obtain high-accuracy video semantic annotation through automatic methods. In this paper, we propose a novel automatic video annotation method, which greatly improves the annotation performance by learning from unlabeled video data, as well as exploring temporal consistency of video sequences. To effectively...
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