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Extreme weather recognition using GoogLeNet can achieve excellent performance, which is far superior to the conventional methods. However, the complexity of GoogLeNet is relatively high. Furthermore, for the small scale data, GoogLeNet usually cannot achieve the performance as the large scale data does. In this paper, a novel dual fine-tuning strategy is proposed to train the GoogLeNet model. Firstly,...
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low-dimensional...
The Fine-grained Vehicle recognition is easily affected by small visual changes. The existing recognition methods have less robustness to these conditions (such as illumination, weather changes, etc.) and the accuracy of vehicle recognition in complex environments cannot achieve a satisfying result. In this paper, a high-accuracy fine-grained vehicle recognition method using Convolutional Neural Network...
Considering the fact that pornographic images are flooding on the web, we propose a pornographic image recognition method based on convolutional neural network. This method can be divided into two parts: coarse detection and fine detection. Because majority of images are normal, we use coarse detecting to quickly identify the normal images with no or fewer skin-color regions and facial images. For...
A compressed pornographic image recognition method is proposed by using incremental learning. For describing pornographic image, visual words are created from low-resolution (LR) image reconstructed from the compressed stream of the pornographic image. Covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic image. At...
In image computing, feature extraction plays a key part for image pattern classification. In this article we adopt discrete fractional Fourier transform (FrFT) for fractional feature extraction. Firstly, a criterion is proposed to determine the FrFT order for an image class so that it may be optimally discriminated from other classes in the FrFT domain, and the transformed features are called fractional...
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