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We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors. The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently “spread-out” over the space. In this work, we propose a regularization term to maximize...
Robust covariant local feature detectors are important for detecting local features that are (1) discriminative of the image content and (2) can be repeatably detected at consistent locations when the image undergoes diverse transformations. Such detectors are critical for applications such as image search and scene reconstruction. Many learning-based local feature detectors address one of these two...
Researchers have recently been performing region of interest detection in such applications as object recognition, object segmentation, and adaptive coding. In this paper, a novel region of interest detection model that is based on visually salient regions is introduced by utilizing the frequency and space domain features in very high resolution remote sensing images. First, the frequency domain features...
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