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Toward Fine-grained image retrieval with Adaptive Deep Learning for Cultural Heritage image

Computer Systems Science & Engineering 2023年第2期44卷 1295-1307页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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Triplet Label Based image retrieval Using Deep Learning in Large Database

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Computer Systems Science & Engineering 2023年第3期44卷 2655-2666页

作者： K.Nithya V.Rajamani Research Scholar Department of Information and Communication EngineeringAnna UniversityChennai600025India Department of Electronics and Communication Engineering Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

Recent days,image retrieval has become a tedious process as the image database has grown very *** introduction of Machine Learning(ML)and Deep Learning(DL)made this process more *** these,the pair-wise label similarity is used tofind the matching images from the *** this method lacks of limited propose code and weak execution of misclassified *** order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative *** improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are *** the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.

关键词： image retrieval deep learning point attention based triplet network correlating resolutions classification region of interest

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Learning Noise-Assisted Robust image Features for Fine-Grained image retrieval

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Computer Systems Science & Engineering 2023年第9期46卷 2711-2724页

作者： Vidit Kumar Hemant Petwal Ajay Krishan Gairola Pareshwar Prasad Barmola Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India School of Computer Science Engineering and Technology Bennett UniversityGreater Noida201310Uttar PradeshIndia

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query *** key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered,and dissimilar images are separated in the low embedding *** works primarily focused on defining local structure loss functions like triplet loss,pairwise loss,***,training via these approaches takes a long training time,and they have poor ***,representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen *** paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these *** the proposed work,class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class *** other instances are treated as negatives within the same *** a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded *** proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results(85.38%recall@1 for CARS-196%and 70.13%recall@1 for CUB-200)compared to other existing methods.

关键词： Convolutional network zero-shot learning fine-grained image retrieval image representation image retrieval intra-class diversity feature learning

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Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 533-549页

作者： Rajakumar Krishnan Arunkumar Thangavelu P.Prabhavathy Devulapalli Sudheer Deepak Putrevu Arundhati Misra School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndiaand Space Applications Centre AhmedabadIndia

Purpose-Extracting suitable features to represent an image based on its content is a very tedious *** in remote sensing we have high-resolution images with a variety of objects on the Earth’s *** distance metric is used to measure the similarity between query and database *** low distance obtained image is indexed at the top as high relevant information to the ***/methodology/approach-This paper aims to develop an automatic feature extraction system for remote sensing image *** texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree(QT)decomposition are developed as feature set to represent the input *** extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user ***-The developed retrieval system performance has been analyzed using precision and recall and F1 *** proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction ***/value-The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT *** features required to represent the image is 207 which is very less dimension compare to other texture *** performance shows superior than the other state of art methods.

关键词： image retrieval Remote sensing Contourlet Texture features Web-based search CBIR Multiscale texture

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A Survey of Crime Scene Investigation image retrieval Using Deep Learning

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Journal of Beijing Institute of Technology 2024年第4期33卷 271-286页

作者： Ying Liu Aodong Zhou Jize Xue Zhijie Xu Xi’an University of Posts and Telecommunications(XUPT) Xi’an 710121China University of Huddersfield Huddersfield HD13DHUK

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal ***,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval ***,there is a lack of systematic research onCSI image retrieval methods and ***,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep *** to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention ***,We analyzed the remain-ing challenges and discussed future work directions in this field.

关键词： crime scene investigation(CSI)image image retrieval deep learning

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Implementation of Texture Based image retrieval Using M-band Wavelet Transform

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Wuhan University Journal of Natural Sciences 2003年第04A期8卷 1107-1110页

作者： Liao Ya-li, Yang Yan, Cao Yang  School of Electronic Information, Wuhan University, Wuhan 430072, Hubei, China Wuhan University School of Electronic Information Wuhan Hubei China (GRID:grid.49470.3e) (ISNI:***)

Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing. The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.

关键词： M-band wavelet transform wavelet moments feature vector image retrieval

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AN EFFECTIVE image retrieval METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS

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Journal of Electronics(China) 2013年第4期30卷 391-400页

作者： Zhang Qin Huang Xiaoqing Liu Wenbo Zhu Yongjun Le Jun College of Automation Engineering Nanjing University of Aeronautics and Astronautics

In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.

关键词： Fractal Coding (FC) Hu moment invariant Kernel Density Estimation (KDE) Variableoptimized bandwidth image retrieval

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New Method of image retrieval Based on Interest image Region Blocks and Comprehensive Features

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Wuhan University Journal of Natural Sciences 2012年第1期17卷 55-60页

作者： PENG Xiaohua ZHU YU LIN Zhongzheng School of Information Science and Engineering EastChina University of Science and Technology Shanghai 200237China . College of Information Science and Technology ShiheziUniversity Shihezi 832003 Xinjiang China

We propose an image retrieval method based on interest image region by asymmetrical blocking. An image is segmented into the interest region and background region on a certain rule. For the interest image regions, the color histogram of the uneven blocks is extracted as the color characteristic. We also collect the mean and variance value of the Gabor filtering results of background blocks as texture features of the background image. Then, the images can be retrieved by synthesizing the image color and texture features. We test our approaches by analyzing the resuits of recall and precision indicators for the Corel image database. The experiment results show that the proposed method performs effectively and accurately, which is more effective to retrieve the distant-view images, and the achieved precision increases by about 10% without loss of the retrieval call compared with some other traditional search methods.

关键词： image retrieval color histogram Gabor filter image block interest region

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A Fast image retrieval Method with Convolutional Neural Networks 36

A Fast Image Retrieval Method with Convolutional Neural Netw...

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第36届中国控制会议

作者： Junfeng Yao Yukai Deng Yao Yu Changyin Sun School of Automation and Electrical Engineering University of Science and Technology Beijing School of Automation Southeast University

ISBN: (纸本)9781538629185

Content-based image retrieval technology is one of the most important research directions in modern image retrieval *** the development of deep learning,the effective features of image can be extracted by well-trained convolution neural networks（CNNs）.Based on the extracted image features,we can measure the similarity between two *** comparing image similarity on large image dataset can lead to high search accuracy at the cost of low search *** this paper,we combined unsupervised learning with approximate nearest neighbor search method to speed up the search *** results of several experiments prove that our method can simultaneously guarantee the accuracy of the search and the speed of retrieval.

关键词： image retrieval CNNs unsupervised learning approximate nearest neighbor

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Document image retrieval based on multi-density features

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Frontiers of Electrical and Electronic Engineering in China 2007年第2期2卷 172-175页

作者： HU Zhilan LIN Xinggang YAN Hong Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Department of Electronic Engineering City University of HongkongHongkongChina School of Electrical and Information Engineering University of SydneyNSW 2006Australia

The development of document image databases is becoming a challenge for document image retrieval *** layout-reconstructed-based methods rely on high quality document images as well as an optical char-acter recognition(OCR)precision,and can only deal with several widely used *** complexity of document layouts greatly hinders layout analysis-based *** paper describes a multi-density feature based algorithm for binary document images,which is independent of OCR or layout *** text area was extracted after prepro-cessing such as skew correction and marginal noise *** the aspect ratio and multi-density features were extract-ed from the text area to select the best candidates from the document image *** results show that this approach is simple with loss rates less than 3%and can efficiently analyze images with different resolutions and dif-ferent input *** system is also robust to noise due to its notes and complex layouts,etc.

关键词： document image image retrieval skew correc-tion multi-density features

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