high-level featureWe propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image ...
This paper addresses the challenge of Multimedia Event Detection by proposing a novel method for high-level and low-level features fusion based on collective classification. Generally, the method consists of three steps: training a classifier from low-level features; encoding high-level features into...
The first step consists in creating sensors from a low-level (color or texture) descriptor, and a Support Vector Machine (SVM) learning to recognize a given concept (for example, "beach" or "road"). The sensor fusion step is the combination of several sensors for each concept. Finally, ...
usually combinelow-leveland high-levelfeatures from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion oflow-leveland 测试金字塔 TestPyramid 测试金字塔 Its essential point is that you should have many morelow-levelunit tests than...
2023/01 Dif-Fusion Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion ModelsJun Yue, Leyuan Fang, Shaobo Xia, Yue Deng, Jiayi Ma TIP2023 Paper/Code2. Extended Diffusion Models In Low-level Vision2.1...
Deep Multi-Model Fusion for Single-Image Dehazing Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, Pheng-Ann Heng Learning Deep Priors for Image Dehazing Yang Liu, Jinshan Pan, Jimmy Ren, Zhixun Su LAP-Net: Level-Aware Progressive Network for Image...
measures the machine vision performance of different methods, provides a low-light image dataset serving both low-level and high-level vision enhancement, and develops an enhanced face detector, our survey reviews the low-light image and video enhancement from different aspects and has the following...
Sophisticated algorithms are then employed to carry out the actual fusion process, which can be broadly classified as pixel-level, feature-level, and decision-level fusion, each encompassing its own methodology and intricacies3. Contemporary image fusion techniques encounter an intricate interplay of ...
Steady-States Visually Evoked Potentials (SSVEP) refer to the sustained rhythmic activity observed in surface electroencephalography (EEG) in response to the presentation of repetitive visual stimuli (RVS). Due to their robustness and rapid onset, SSVEP
1.Correlation of Image Low-level Features and High-level Semantics Using SVM;基于SVM的图像低层特征与高层语义的关联 2.A new method for correlating image low-level feature with high-level semantic based on Fuzzy Support Vector Machines (FSVM) is proposed, aiming at overcoming the considerable gap...