Weakly Supervised Domain-Specific Color Naming Based on Attention
Submitted at International Conference on Pattern Recognition (ICPR), 2018
Abstract: The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
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Suitability of Real-Time Image under Complicated Environment Based on Contourlet in SMN
Accepted by Intelligent Systems and Knowledge Engineering (ISKE), 2015
Abstract: Judging whether the real-time image under complicated environment is suitable is a challenging problem in scene matching navigation, which contributes to ensure the navigation precision and decrease computational complexity. This paper proposes a novel method for analyzing the
suitability of real-time image under complicated environment based on Contourlet by taking advantage of the characteristic of multi-direction and multi-scale of Contourlet, where the complicated environment focus on motion blur, illumination variation, occlusion of cloud and fog. Firstly, real-time image is transformed on 4-layer Contourlet, and the obtained coefficients are parameterized by Generalized Gaussian Distribution, forming a 62 - dimension feature vector. Then the relationship between the feature vector and the objective evaluation index of suitability is trained by support vector
machine, to build the prediction model of suitability of realtime image under complicated environment. Finally, experiments are performed on image database picked from Google Earth. The experiments clearly demonstrate that the proposed algorithm is simple but effective for real-time image quality assessment in scene matching navigation.
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