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In the task of hyperspectral image classification, band selection is often adopted to select a subset of informative bands to reduce the computation and storage cost. We propose a supervised band selection method which allows calculation of a discriminative weight for each band. Specifically, we consider discriminative bands as those that contribute more positive scores to a one-class classifier than...
L1-norm maximization based Discriminant Locality Preserving Projection (DLPP-L1) is shown to be effective and robust to the outliers in given data, but DLPP-L1 is based on the vector space, so it has to convert those 2D matrices into high-dimensional 1D vectorized representations when handing images. But such transformation usually destroys the topology structures of images pixels, which can decrease...
To effectively and efficiently retrieve desired images from a large image database, an intuitional and common type of approaches is text-based image retrieval which accesses the images by comparing conceptual terms of a query and image data. Unfortunately, this type of image retrieval is not easy to earn users' satisfactions due to the problem of the image database maintenance. Another useful type...
In this paper, we propose a L1-Norm driven Semi-Supervised Local Discriminant Projection (S2LDP-L1) for robust dimensionality reduction and image representation. For feature learning, our S2LDP-L1 approach aims at compacting local within-class divergence and separating local betweenclass divergence at the same time in addition to possessing the locality preserving power over all training data. To...
Vibration signal measured from machinery is often heavily interfered with by various noises. This paper puts forward a joint method to reduce noises, acquire the enhanced signals from the decomposed subbands and extract the incipient fault features. First, the signals are denoised by the method of singular value decomposition (SVD). Then, the denoised signal is decomposed into four layers by undecimated...
The local feature (e.g. SIFT) and Bag of Words (BOW) model play key roles in achieving a state-of-the-art performance for image classification. Although we realize that utilizing extra color information will undoubtedly boost the local feature, there still have not been any research that have carefully focused on how to efficiently transfer this color boosted local feature into a boosted BOW. In this...
In view of the increasing demand of recognition system for paper currency number, to develop a type of number recognition system based on CIS and DSP. The hardware is composed of CIS and DSP which control image acquisition and process. The software is composed of image acquisition, character correction and recognition. To recognize character with noise pollution rapidly and accurately, a novel approach...
In this paper, an effective synthetic aperture radar image segmentation method is proposed. Gaussian mixture models optimized by greedy expectation maximization algorithm are applied. The immune genetic algorithm is employed to initialize greedy expectation maximization algorithm and search the optimal values in the whole range, instead of general k-means algorithm, which is different from the traditional...
We present a simple and efficient method to seamlessly stitch the images according to a stitching line based on an energy map. The power of this method lies in the integration of many superior features and at the same time, it is still efficient. One striking feature of our approach is that it is based on the philosophy that human eyes mostly notice only the salient features of an image and the stitching...
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