In response to the challenge of limited training samples for synthetic aperture radar (SAR) target detection, a SAR image expansion method based on improved cycle generative adversarial network (CycleGAN) is proposed. First, the structure of the CycleGAN is refined to enhance training efficiency. Then, instance normalization (IN) is incorporated into the residual unit of the CycleGAN to improve the realism of generated SAR images. Finally, SAR target recognition experiments were conducted using the single shot multibox detection (SSD) detection network with VoVNet structure. The experimental results prove that the data expansion method based on CycleGAN can improve the recognition accuracy of SAR targets.

Landmark summarization with diverse viewpoints is very important in landmark retrieval, as it can create a comprehensive description of a landmark for users. In this paper we present an approach for summarizing a collection of landmark images from diverse viewpoints. First, we group landmark images with content overlap by viewpoint album (VA) generation. Second, we model the relative viewpoint of each image within the VA based on the spatial layout of distinctive descriptors of a landmark. Third, we express the relative viewpoint of an image with a 4-D viewpoint vector, including horizontal, vertical, scale, and rotation. Finally, we summarize the landmarks in terms of viewpoints. Experimental results show the effectiveness of the proposed landmark summarization approach.

Nowadays, it is very convenient to capture photos by a smart phone. As using, the smart phone is a convenient way to share what users experienced anytime and anywhere through social networks, it is very possible that we capture multiple photos to make sure the content is well photographed. In this paper, an effective scalable mobile image retrieval approach is proposed by exploring contextual salient information for the input query image. Our goal is to explore the high-level semantic information of an image by finding the contextual saliency from multiple relevant photos rather than solely using the input image. Thus, the proposed mobile image retrieval approach first determines the relevant photos according to visual similarity, then mines salient features by exploring contextual saliency from multiple relevant images, and finally determines contributions of salient features for scalable retrieval. Compared with the existing mobile-based image retrieval approaches, our approach requires less bandwidth and has better retrieval performance. We can carry out retrieval with <200-B data, which is <5% of existing approaches. Most importantly, when the bandwidth is limited, we can rank the transmitted features according to their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.

Owing to the portable and excellent phone camera, people now prefer to take photos and upload them by mobile phone. Content based image retrieval is effective for users to obtain relevant information about a photo. Taking the limited bandwidth and instability into account, we propose an effective scalable mobile image retrieval approach in this paper. The proposed mobile image retrieval algorithm first determines the relevant photos according to visual similarity in mobile end, then mines salient visual words by exploring saliency from multiple relevant images, and finally we determine the contribution order of salient visual words for scalable retrieval. In server terminal, spatial verification is performed to re-rank the results. Compared to the existing approaches of mobile image retrieval, our approach transmits less data and reduces the computational cost of spatial verification. Most importantly, when the bandwidth is limited, we can transmit a part of features according their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.

Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to various channel condition is proposed. The proposed algorithm contains three contributions: (1) to achieve instant retrieval under various channel capacity, we adjust transmission load by sparseness instead of codebook size; (2) we introduce hierarchical sparse coding into our retrieval workflow, where original codebook is transformed into a tree-structured dictionary which implies elements' priority; (3) we propose transmission priority ranking schemes that is adaptive to specific query. Experiment results show that the proposed algorithm outperforms BoW and Lasso based algorithm under different parameter settings. Retrieval results under different channel limitation validate the scalability of our method.