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The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. [ABSTRACT FROM AUTHOR]

This paper presents a novel, fully automatic method to grayscale images colorization using a scene guided neural network. In our proposed method, given a training set of both grayscale images and their corresponding color images, we first extract features of each grayscale pixel. These features, together with the corresponding RGB values of that pixel are input to train a colorization neural network for each given scene. To improve the performance of colorization, in both speed and results, we further classify the input and training images into different scene classes. We adopt a linear image classification method to generate a scene guided codebook and use it to determine the scene class of the input image. The preliminary colorization result is then generated by the corresponding trained neural network of the scene class of the input image. Finally, an image guided filter is used to refine colorized images. Inspired by the recent success in deep learning techniques which provide stabilizing modeling of large-scale medical image data, the proposed paper formulating the enhancement and colorization problem, so that colorization techniques can be directly used to ensure medical images with high quality. The experimental results on a broad range of images demonstrate that our method has better colorization performance as compared to that of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]