Ruohan Meng^1,2, Zhili Zhou^1,2, Qi Cui^1,2, Xingming Sun^1,2,*, Chengsheng Yuan^1,2,3

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 43-48, 2019, DOI:10.32604/jihpp.2019.05797

Abstract At present, the coverless information hiding has been developed. However, due to the limited mapping relationship between secret information and feature selection, it is challenging to further enhance the hiding capacity of coverless information hiding. At the same time, the steganography algorithm based on object detection only hides secret information in foreground objects, which contribute to the steganography capacity is reduced. Since object recognition contains multiple objects and location, secret information can be mapped to object categories, the relationship of location and so on. Therefore, this paper proposes a new steganography algorithm based on object detection and relationship mapping, which… More >

Yongxiang Gu¹, Beijing Chen¹, Xu Cheng^1,*, Yifeng Zhang^2,3, Jingang Shi⁴

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 23-33, 2019, DOI:10.32604/jihpp.2019.05938

Abstract Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning. First, we categorize the existing deep learning based trackers into three classes according to network structure, network function and network training. For each categorize, we analyze papers in different categories. Then, we conduct extensive experiments to compare the representative methods on the popular OTB-100, TC-128 and VOT2015 benchmarks. Based on our observations. We conclude that: (1) The usage of the convolutional neural network (CNN) model could significantly improve the tracking performance. (2) The trackers… More >

Liming Chen¹, Bosi Wang^1,*, Weijie Yu¹, Xu Fan¹

Journal of New Media, Vol.1, No.3, pp. 115-126, 2019, DOI:10.32604/jnm.2019.08581

Abstract With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.… More >

Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³

Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Maoyang Zou^1,2, Jinrong Hu², Huan Zhang², Xi Wu², Jia He², Zhijie Xu³, Yong Zhong^1,*

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 511-525, 2019, DOI:10.32604/cmc.2019.05912

Abstract For image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy and interventional radiology, one of the important techniques is medical image registration. In our study, we propose a learning-based approach named “FIP-CNNF” for rigid registration of medical image. Firstly, the pixel-level interest points are computed by the full convolution network (FCN) with self-supervise. Secondly, feature detection, descriptor and matching are trained by convolution neural network (CNN). Thirdly, random sample consensus (Ransac) is used to filter outliers, and the transformation parameters are found with the most inliers by iteratively fitting transforms. In addition, we propose “TrFIP-CNNF” which uses transfer learning and fine-tuning… More >

Xianyu Wu¹, Chao Luo¹, Qian Zhang², Jiliu Zhou¹, Hao Yang^{1, 3, *}, Yulian Li¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

Maosen Wang¹, Shaozhang Niu^1,*, Zhenguang Gao²

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 781-794, 2019, DOI:10.32604/cmc.2019.05595

Abstract Scene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition task of sequence texts. Our… More >

Feng Xu¹, Xuefen Zhang^2,*, Zhanhong Xin¹, Alan Yang³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 697-709, 2019, DOI:10.32604/cmc.2019.05375

Abstract Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far… More >

Xudong Hong¹, Xiao Zheng^1,*, Jinyuan Xia¹, Linna Wei¹, Wei Xue¹

CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 379-389, 2019, DOI:10.32604/cmc.2019.04059

Abstract To acquire non-ferrous metals related news from different countries’ internet, we proposed a cross-lingual non-ferrous metals related news recognition method based on CNN with a limited bilingual dictionary. Firstly, considering the lack of related language resources of non-ferrous metals, we use a limited bilingual dictionary and CCA to learn cross-lingual word vector and to represent news in different languages uniformly. Then, to improve the effect of recognition, we use a variant of the CNN to learn recognition features and construct the recognition model. The experimental results show that our proposed method acquires better results. More >