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Scene classification is a hot topic in pattern recognition and computer vision area. In this paper, based on the past research on vision neuroscience, we proposed a new biologically inspired feature method for scene image classification. The new feature accounts for the visual processing from simple cell to complex cell in V1 area, and also the spatial layout for scene gist signature. It provides...
In recent years there is a growing interest in the study of sparse representation for signals. This article extends this research into a novel model for object classification tasks. In this model, we first apply the non-negative K-SVD algorithm to learning the discriminative dictionaries using very few training samples and then represent a test image as a linear combination of atoms from these learned...
The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, first, a new method called clustering-based locally linear embedding (CLLE) is proposed, which is able to solve the problem of high time consuming of LLE and preserve the data topology at the same time. Then, how the proposed method achieves decreasing...
In this paper, an adaptive edge-based text detection approach in images and video frames is proposed. The proposed approach can adopt different edge detection methods according to the image background complexity. It mainly consists of four stages: Firstly, images are classified into different background complexities. Secondly, different edge detectors are applied on the images according to their background...
In this paper, we propose a new approach for unconstrained handwritten character recognition based on wavelet energy density feature (WEDF) and multilayer neural network. Unlike other method taking the wavelet coefficients directly as features, our method using the wavelet energy density features instead. The proposed approach consists of a feature extraction stage for extracting wavelet energy density...
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