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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Content-Based Image Retrieval Using Deep Search
verfasst von : Zhengzhong Zhou, Liqing Zhang
Erschienen in: Neural Information Processing
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Abstract
The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find images with relevant tags regardless of contents in the images. Generally, people are more interested in images with similarity both in contours and high-level concepts. Therefore, we propose a new strategy called Deep Search to meet this requirement. It mines knowledge from the similar images of original queries, in order to compensate for the missing information in feature extraction process. To evaluate the performance of Deep Search approach, we apply this method to three different CBIR systems (HOF [5], HOG and GIST) in our experiments. The results show that Deep Search greatly improves the performance of original algorithms, and is not restricted to any particular methods.