scholarly journals A Survey on Image Retrieval Techniques

2020 ◽  
Author(s):  
Lalitha K ◽  
Murugavalli S
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Medical Tourism ◽  
Digital Images ◽  
Rapid Development ◽  
Vital Role ◽  
Content Based Image Retrieval ◽  
Processing Methods ◽  
Exponential Order

In the recent years, with the rapid development of technologies production, digital images tend to grow in exponential order. This makes the challenge in retrieving image. Image retrieval plays a vital role in various fields such as Medical, Tourism promotion, Forensics Lab etc. In order to handle these rapid growth efficient images retrieval methods need to be developed. This paper is presented with the survey of different Image retrieval techniques which used various techniques from visual features to the latest deep learning with Convolutional Neural Network(CNN) which become the best approach for image retrieval with number of layers applicable for large database. This survey concretes the way to understand the processing methods of Content Based Image Retrieval (CBIR).

CBIR-CNN: Content Based Image Retrieval on celebrity data using Deep Convolution Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Facial Image ◽  
Huge Amount ◽  
Proposed Model ◽  
Deep Convolution Neural Network

Background: Finding region of interest in an image and content-based image analysis has been a challenging task for last two decades. With the advancement in image processing, computer vision field and huge amount of image data generation, to manage this huge amount of data Content-Based Image Retrieval System (CBIR) has attracted several researchers as a common technique to manage this huge amount of data. It is an approach of searching user interest, based on visual information present in an image. The requirement of high computation power and huge memory limits deployment of CBIR technique in real-time scenarios. Objective: In this paper an advanced deep learning model is applied for CBIR on facial image data. We design a deep convolution neural network architecture where activation of convolution layer is used for feature representation and include max pooling as feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information. Method: Existing CBIR approaches primarily consider colour, texture and low-level features for mapping and localizing image segments. While deep learning has shown high performance in numerous fields of research, its application in CBIR is still very limited. Human face contains significant information to be used in a content driven task and applicable to various applications of computer vision and multimedia systems. In this research work, a deep learning-based model has been discussed for content-based image retrieval (CBIR). In CBIR, there are two important things 1) classification and 2) retrieval of image based on similarity. For the classification purpose a four-convolution layer model has been proposed. For the calculation of the similarity Euclidian distance measure has been used between the images. Results: Proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over facial dataset. The proposed method provided satisfactory results from the experiment. It outperforms other CNN-based models and other unsupervised techniques used for CBIR. The proposed method provided satisfactory results from the experiment and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50 and MobileNet. Moreover, the performance of proposed model has been compared with pre-trained models in terms of accuracy, storage space and inference time.

Deep learning for fast bronze inscription image retrieval

2020 ◽  
Vol 4 (4) ◽  
pp. 291-296
Author(s):  
Ziyang Wang ◽  
Wei Zheng ◽  
Youguang Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Local Features ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Euclidean Metric

Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.

scholarly journals Neural network feature learning based on image self-encoding

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092165
Author(s):  
Yangyang Liu ◽  
Minghua Tian ◽  
Chang Xu ◽  
Lixiang Zhao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Image Coding ◽  
Rapid Development ◽  
Feature Learning ◽  
Feature Representation ◽  
The Neural Network ◽  
Network Feature ◽  
Feature Expression

With the rapid development of information technology and the arrival of the era of big data, people’s access to information is increasingly relying on information such as images. Today, image data are showing an increasing trend in the form of an index. How to use deep learning models to extract valuable information from massive data is very important. In the face of such a situation, people cannot accurately and timely find out the information they need. Therefore, the research on image retrieval technology is very important. Image retrieval is an important technology in the field of computer vision image processing. It realizes fast and accurate query of similar images in image database. The excellent feature representation not only can represent the category information of the image but also capture the relevant semantic information of the image. If the neural network feature learning expression is combined with the image retrieval field, it will definitely improve the application of image retrieval technology. To solve the above problems, this article studies the problems encountered in deep learning neural network feature learning based on image self-encoding and discusses its feature expression in the field of image retrieval. By adding the spatial relationship information obtained by image self-encoding in the neural network training process, the feature expression ability of the selected neural network is improved, and the neural network feature learning based on image coding is successfully applied to the popular field of image retrieval.

scholarly journals Content Based Image Retrieval from Auto Encoders Using Keras and Tensor Flow Python API a Deep Learning Technique

2020 ◽  
Vol 5 (9) ◽  
pp. 869-871
Author(s):  
Vinitha V ◽  
Velantina . V
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
New Methods ◽  
Methods And Techniques ◽  
Learning Technique ◽  
Image Retrieval System

As the technology is evolving new methods and techniques are determined and implemented in a smart way to improve and achieve a greater insight in this smart era. The retrieval of image is popularly growing in this emerging trend. In this paper we have used how to build a very simple image retrieval system using a special type of Neural Network called auto encoders. Here the images can be retrieved with visual contents textures, shape and this method of image retrieval is called content based image retrieval.

scholarly journals Deep Learning for Plant Classification and Content-Based Image Retrieval

2019 ◽  
Vol 19 (1) ◽  
pp. 88-100
Author(s):  
Bálint Pál Gyires-Tóth ◽  
Márton Osváth ◽  
Dávid Papp ◽  
Gábor Szűcs
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Search Task ◽  
Feature Learning ◽  
Content Based Image Retrieval ◽  
Network Architectures ◽  
Average Precision ◽  
Test Set ◽  
Fully Connected

Abstract The main goal of the present research is to classify images of plants to species with deep learning. We used convolutional neural network architectures for feature learning and fully connected layers with logsoftmax output for classification. Pretrained models on ImageNet were used, and transfer learning was applied. In the current research image sets published in the scope of the PlantCLEF 2015 challenge were used. The proposed system surpasses the results of all top competitors of the challenge by 8% and 7% at observation and image levels, respectively. Our secondary goal was to satisfy the users’ needs in content-based image retrieval to give relevant hits during species search task. We optimized the length of the returned lists in order to maximize MAP (Mean Average Precision), which is critical to the performance of image retrieval. Thus, we achieved more than 50% improvement of MAP in the test set compared to the baseline.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

scholarly journals Deep Learning for Content-Based Image Retrieval

2014 ◽  
Author(s):  
Ji Wan ◽  
Dayong Wang ◽  
Steven Chu Hong Hoi ◽  
Pengcheng Wu ◽  
Jianke Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Content Based Image Retrieval

scholarly journals Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 487 ◽  
Author(s):  
Trang Thi Kieu Tran ◽  
Taesam Lee ◽  
Ju-Young Shin ◽  
Jong-Suk Kim ◽  
Mohamad Kamruzzaman
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Human Life ◽  
Time Series Prediction ◽  
Vital Role ◽  
Maximum Temperature ◽  
Hyperparameter Optimization ◽  
Meta Learning

Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).

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