An Efficient Deep Learning-based Content-based Image Retrieval Framework  

作　　者：M.Sivakumar N.M.Saravana Kumar N.Karthikeyan 

机构地区：[1]Department of CSE,Sri Krishna College of Engineering and Technology,Anna University,Coimbatore,Tamil Nadu,641008,India [2]Department of CSE,M.Kumarasamy College of Engineering,Anna University,Karur,Tamil Nadu,639113,India

出　　处：《Computer Systems Science & Engineering》2022年第11期683-700,共18页计算机系统科学与工程（英文）

摘　　要：The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provid

关 键 词：Content based image retrieval(CBIR) improved gray level cooccurrence matrix(GLCM) hierarchal and fuzzy C-means(HFCM)algorithm deep learning based enhanced convolution neural network(DLECNN) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]