Authors:
Bo Yan; Weimin Tan; Ke Li; Qi Tian
Publication:
This paper is included in the IEEE Transactions on Image Processing, Volume: 26, Issue: 5, Page(s): 2454-2465, May 2017.
Abstract:
Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k data sets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.
[pdf] Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting