Jian Tang, Zhihua Xia^*, Lan Wang, Chengsheng Yuan, Xueli Zhao

Journal on Big Data, Vol.3, No.1, pp. 21-33, 2021, DOI:10.32604/jbd.2021.015892

Abstract As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We… More >

Yan Zhou, Lili Pan^*, Rongyu Chen, Weizhi Shao

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 77-86, 2020, DOI:10.32604/jihpp.2020.010486

Abstract In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods. However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency. We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval. Firstly, the improved network is based on the existing deep model to build a more profound and broader network… More >

Rongyu Chen, Lili Pan^*, Yan Zhou, Qianhui Lei

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472

Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA… More >

Xuefei Du^*, Xuyu Xiang

Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 11-19, 2020, DOI:10.32604/jihpp.2020.010114

Abstract Citrus anthracnose is a common fungal disease in citrus-growing areas in China, which causes very serious damage. At present, the manual management method is time-consuming and labor-consuming, which reduces the control effect of citrus anthracnose. Therefore, by designing and running the image retrieval system of citrus anthracnose, the automatic recognition and analysis of citrus anthracnose control were realized, and the control effect of citrus anthracnose was improved. In this paper, based on the self-collected and collated citrus anthracnose image database, we use three image features to realize an image retrieval system based on citrus anthracnose through SMV, AP clustering optimization.… More >

Rong Duan¹, Junshan Tan^{1, *}, Jiaohua Qin¹, Xuyu Xiang¹, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2335-2350, 2020, DOI:10.32604/cmc.2020.012161

Abstract In recent years, with the massive growth of image data, how to match the image required by users quickly and efficiently becomes a challenge. Compared with single-view feature, multi-view feature is more accurate to describe image information. The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval. In this paper, a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed. By learning the data correlation between different views, this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval… More >

Qiuju Ji¹, Peipeng Yu¹, Zhihua Xia^{1, *}

Journal on Big Data, Vol.2, No.1, pp. 33-51, 2020, DOI:10.32604/jbd.2020.01004

Abstract Aprivacy-preserving search model for JPEG images is proposed in paper, which uses the bag-of-encrypted-words based on QDCT (Quaternion Discrete Cosine Transform) encoding. The JPEG image is obtained by a series of steps such as DCT (Discrete Cosine Transform) transformation, quantization, entropy coding, etc. In this paper, we firstly transform the images from spatial domain into quaternion domain. By analyzing the algebraic relationship between QDCT and DCT, a QDCT quantization table and QDTC coding for color images are proposed. Then the compressed image data is encrypted after the steps of block permutation, intra-block permutation, single table substitution and stream cipher. At… More >

Chunjing Wang^*, Li Liu, Yanyan Tan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1061-1083, 2020, DOI:10.32604/cmes.2020.010198

Abstract The implementation of content-based image retrieval (CBIR) mainly depends on two key technologies: image feature extraction and image feature matching. In this paper, we extract the color features based on Global Color Histogram (GCH) and texture features based on Gray Level Co-occurrence Matrix (GLCM). In order to obtain the effective and representative features of the image, we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively. And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to… More >

Yugang Li^{1, 2, *}, Yongbin Wang¹, Zhe Chen², Yuting Zhu³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1575-1589, 2020, DOI:10.32604/cmc.2020.07451

Abstract Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding. Most previous methods have focused on recognizing local predictions of the relationships. But real-world image relationships often determined by the surrounding objects and other contextual information. In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection. The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the… More >

Jiaohua Qin¹, Yusi Cao¹, Xuyu Xiang^{1, *}, Yun Tan¹, Lingyun Xiang², Jianjun Zhang³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 389-399, 2020, DOI:10.32604/cmc.2020.07819

Abstract With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information of images, which hashes the… More >

Chaowen Lu^1,a, Feifei Lee^1,a,*, Lei Chen¹, Sheng Huang¹, Qiu Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 593-608, 2019, DOI:10.32604/cmes.2019.07796

Abstract Image retrieval has become more and more important because of the explosive growth of images on the Internet. Traditional image retrieval methods have limited image retrieval performance due to the poor image expression abhility of visual feature and high dimension of feature. Hashing is a widely-used method for Approximate Nearest Neighbor (ANN) search due to its rapidity and timeliness. Meanwhile, Convolutional Neural Networks (CNNs) have strong discriminative characteristics which are used for image classification. In this paper, we propose a CNN architecture based on improved deep supervised hashing (IDSH) method, by which the binary compact codes can be generated directly.… More >