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Figure 1: L 0 smoothing accomplished by global small-magnitude gradient removal. Our method suppresses low-amplitude details. Meanwhile it globally retains and sharpens salient edges. Even the high-contrast thin edges on the tower are preserved. Abstract We present a new image editing method, particularly effective for sharpening major edges by increasing(More)
Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because(More)
Decolorization - the process to transform a color image to a grayscale one - is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image processing applications. In this paper, we propose an optimization approach aiming at maximally preserving the original color contrast. Our main contribution is to alleviate(More)
Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. " man riding bicycle " and " man pushing bicycle "). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on(More)
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF)(More)
Decolorization -- the process to transform a color image to a grayscale one -- is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image and video processing applications. While recent research focuses on retaining meaningful visual features and color contrast, less attention has been paid to the complexity(More)
Converting color images into grayscale ones suffer from information loss. In the meantime, it is one fundamental tool indispensable for single channel image processing, digital printing, and monotone e-ink display. In this paper, we propose an optimization framework aiming at maximally preserving color contrast. Our main contribution is threefold. First, we(More)
Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. Never adequately addressed, this twoclass classification problem is by no means trivial given the great variety of outdoor images. Our weather feature combines special cues after properly encoding them into feature vectors. They(More)
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however, faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper, we propose a new online framework enabling the use of &#x2113;<sup>1</sup>(More)
We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. This problem makes mid-level representation, even after pooling, not distinct enough(More)