Automatic classification of digital patent images is significant for improving the efficiency of patent examination and management. In this paper, we propose a new patent image classification method based on an enhanced deep feature representation. Convolutional neural networks (CNN) is novelly applied to the patent image classification. The synergy between deep learning and traditional handcraft feature is explored. Specifically, the deep feature is first learned from massive patent image samples by AlexNet. Then such deep learning feature is further enhanced by fusing with two kinds of typical handcraft features including local binary pattern (LBP) and adaptive hierarchical density histogram (AHDH). In order to obtain a more compact feature representation, dimension of the fused feature is subsequently reduced by PCA. Finally, the patent image classification is conducted by a series of SVM classifier. Statistical test results on a large-scale image set show that the state-of-the-art performance is achieved by our proposed patent image classification method.
Recently, T. Celik proposed an effective image contrast enhancement (CE) method based on spatial mutual information and PageRank (SMIRANK). According to the state-of-the-art evaluation criteria, it achieves the best visual enhancement quality among existing global CE methods. However, SMIRANK runs much slower than the other counterparts, such as histogram equalization (HE) and adaptive gamma correction. Low computational complexity is also required for good CE algorithms. In this paper, we novelly propose a fast SMIRANK algorithm, called FastSMIRANK. It integrates both spatial and gray-level downsampling into the generation of pixel value mapping function. Moreover, the computation of rank vectors is speeded up by replacing PageRank with a simple yet efficient row-based operation of mutual information matrix. Extensive experimental results show that the proposed FastSMIRANK could accelerate the processing speed of SMIRANK by about 20 times, and is even faster than HE. Comparable enhancement quality is preserved simultaneously.
Many works have devoted to exploring local region information including both the information of the local features in local region and their spatial relationships, but none of these can provide a compact representation of the information. To achieve this, we propose a new approach named Local Visual Similarity (LVS). LVS first calculates the similarities among the local features in a local region and then forms these similarities as a single vector named LVS descriptor. In our experiments, we show that LVS descriptor can preserve local region information with low dimensionality. Besides, experimental results on two public datasets also demonstrate the effectiveness of LVS descriptor.
KEYWORDS: Visualization, Associative arrays, Principal component analysis, Image classification, Performance modeling, Image compression, Visual process modeling, Computer programming, Digital image processing, Current controlled current source
Large visual dictionaries are often used to achieve good image classification performance in bag-of-features (BoF) model, while they lead to high computational cost on dictionary learning and feature coding. In contrast, using small dictionaries can largely reduce the computational cost but result in poor classification performance. Some works have pointed out that pooling locally across feature space can boost the classification performance especially for small dictionaries. Following this idea, various pooling strategies have been proposed in recent years, but they are not good enough for small dictionaries. In this paper, we present a unified framework of pooling operation, and propose two novel pooling strategies to improve the performance of small dictionaries with low extra computational cost. Experimental results on two challenging image classification benchmarks show that our pooling strategies outperform others in most cases.
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