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.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (61272251), the Key Basic Research Program of Shanghai Municipality, China (15JC1400103) and the National Basic Research Program of China (2015CB856004).
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Zhou, Z., Zhang, L. (2016). Content-Based Image Retrieval Using Deep Search. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_70
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DOI: https://doi.org/10.1007/978-3-319-46672-9_70
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