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Concept Extraction from Patent Images Based on Recursive Hybrid Classification
Abstract
Recently, the intellectual property and information retrieval communities have shown interest in patent image analysis, which could augment the current practices of patent search by image classification and concept extraction. This article presents an approach for concept extraction from patent images, which relies upon recursive hybrid (text and visual-based) classification. To evaluate this approach, we selected a dataset from the footwear domain.
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A patent always contains some images along with the text. Many text based systems have been developed to search the patent database. In this paper, we describe PATSEEK that is an image based search system for US patent database. The objective is to let the user check the similarity of his query image with the images that exist in US patents. The user can specify a set of key words that must exist in the text of the patents whose images will be searched for similarity. PATSEEK automatically grabs images from the US patent database on the request of the user and represents them through an edge orientation autocorrelogram. L1 and L2 distance measures are used to compute the distance between the images. A recall rate of 100% for 61% of query images and an average 32% recall rate for rest of the images has been observed.
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Mechanical patent searching: a moving target
- De Marco