Along with the exponential growth of high-performance mobile devices, on-device Mobile Landmark Recognition (MLR) has recently attracted increasing research attention. However, the latency and accuracy of automatic recognition remain as bottlenecks against its real-world usage. In this article, we introduce a novel framework that combines interactive image segmentation with multifeature fusion to achieve improved MLR with high accuracy. First, we propose an effective vector binarization method to reduce the memory usage of image descriptors extracted on-device, which maintains comparable recognition accuracy to the original descriptors. Second, we design a location-aware fusion algorithm that can fuse multiple visual features into a compact yet discriminative image descriptor to improve on-device efficiency. Third, a user-friendly interaction scheme is developed that enables interactive foreground/background segmentation to largely improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithms for on-device MLR applications.
Mobile Visual Location Recognition (MVLR) has attracted a lot of researchers' attention in the past few years. Existing MVLR applications commonly use Query-by-Example (QBE) based image retrieval principle to fulfill the location recognition task. However, the QBE framework is not reliable enough due to the variations in the capture conditions and viewpoint changes between the query image and the database images. To solve the above problem, we make following contributions to the design of a panorama based on-device MVLR system. Firstly, we design a heading (from digital compass) aware BOF (Bag-of-features) model to generate the descriptors of panoramic images. Our approach fully considers the characteristics of the panoramic images and can facilitate the panorama based on-device MVLR to a large degree. Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method. While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly. Thirdly, we also release a panoramas database as well as a set of test panoramic quires which can be used as a new benchmark to facilitate further research in the area. Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.
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