Abstract
In this paper, we proposes a method which use locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) for patent image classification. Patent images usually have no texture and color information which makes it hard for recognition. Many methods based on contour, shape or edge of image have been proposed, however, as far as we are concerned, our method is the first attempt using coding features for patent image classification. First, we extract dense Scale-Invariant Feature Transformation (SIFT) features and use k-means clustering to train a codebook which based on LLC. Second, we divide the image into increasing fine sub-regions and generate the feature for each sub-region as SPM do. Finally, we use a linear SVM classifier for patent image classification. The experiment on a public database for patent image has demonstrated our method has achieved the stated-of-the-art accuracy rate of 94.2%. It proves model based on SPM and LLC have bright future in patent image recognition.
Published Version
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