Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval

To effectively retrieve objects from large corpus with high accuracy is a challenge task. In this paper, we propose a method that propagates visual feature level similarities on a Markov random field (MRF) to obtain a high level correspondence in image space for image pairs. The proposed correspondence between image pair reflects not only the similarity of low-level visual features but also the relations built through other images in the database and it can be easily integrated into the existing bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate our method on the standard Oxford-5K, Oxford-105K and Paris-6K dataset. The experiment results show that the proposed method significantly improves the retrieval accuracy on three datasets and exceeds the current state-of-the-art retrieval performance

Passivity-based tracking control of an omnidirectional mobile robot using only one geometrical parameter

This paper presents an output feedback tracking control scheme for a three-wheeled omnidirectional mobile robot, based on passivity property and a modified generalized proportional integral (GPI) observer. The proposed control approach is attractive from an implementation point of view, since only one robot geometrical parameter (i.e., contact radius) is required. Firstly, a nominal dynamic model is given and the passivity property is analyzed. Then the controller is designed based on passivity property and a modified GPI observer. The controller design objective is to preserve the passivity property of the robot system in the closed-loop system, which is conceptually different from the traditional model-based control methodology. Particularly, the designed control system takes full advantage of the robot natural damping. Therefore, only considerably small or non differential feedback is needed. In addition, theoretical analysis is given to show the closed-loop stability behavior. Finally, experiments are conducted to validate the effectiveness of the proposed control system design in both tracking and robustness performance

A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Although high dynamic range (HDR) is now a common format of digital images, limited work has been done for HDR source forensics. This paper presents a method based on a convolutional neural network (CNN) to detect the source of HDR images, which is built in the discrete cosine transform (DCT) domain. Specifically, the input spatial image is converted into DCT domain with discrete cosine transform. Then, an adaptive multi-scale convolutional (AMSC) layer extracts features related to HDR source forensics from different scales. The features extracted by AMSC are further processed by two convolutional layers with pooling and batch normalization operations. Finally, classification is conducted by a fully connected layer with Softmax function. Experimental results indicate that the proposed DCT-CNN outperforms the state-of-the-art schemes, especially in accuracy, robustness, and adaptability

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods