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Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. In this paper, we propose a saliency detection method based on optimization for RGBD images. With RGBD images, our method utilizes the depth channel to enhance the identification of background and foreground regions. We firstly generate new depth image by using non-linear transformation...
We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition...
Image emotion analysis is a new and challenging research direction that gains more and more attention in the research community. Most previous works in this field only use common or generic features, and have hard restrictions on training images, such as scale, resolution, etc. Inspired by scale-space theories and psychology theories of color, we propose a procedure to extract interpretive features...
As one of the basic properties of image, texture undoubtedly affect the image saliency. We introduce a texture contrast based salient region detection method, which first divide an input image into several nearly uniform super pixels, then analyze the texture feature and calculate the texture differences between regions to detect salient region. In order to obtain a better saliency map, we also optimize...
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