Image retrieval based on fireworks algorithm optimizing convolutional neural network

2018 ◽

Vol 7 (3.1) ◽

pp. 13

Author(s):

Raveendra K ◽

R Vinoth Kanna

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Document Image ◽

Retrieval Process ◽

Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

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Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network

IEEE Transactions on Semiconductor Manufacturing ◽

10.1109/tsm.2018.2795466 ◽

2018 ◽

Vol 31 (2) ◽

pp. 309-314 ◽

Cited By ~ 67

Author(s):

Takeshi Nakazawa ◽

Deepak V. Kulkarni

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Pattern Classification ◽

Defect Pattern

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An Advanced Relevance Feedback Method to Improve Performance of CBIR using Convolutional Neural Network and Comprehensive Values

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b2741.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 5427-5438

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Large Scale ◽

Activation Function ◽

Image Feature ◽

Similarity Measurement ◽

Query Image ◽

Image Production

Content-Based Image Retrieval (CBIR) is extensively used technique for image retrieval from large image databases. However, users are not satisfied with the conventional image retrieval techniques. In addition, the advent of web development and transmission networks, the number of images available to users continues to increase. Therefore, a permanent and considerable digital image production in many areas takes place. Quick access to the similar images of a given query image from this extensive collection of images pose great challenges and require proficient techniques. From query by image to retrieval of relevant images, CBIR has key phases such as feature extraction, similarity measurement, and retrieval of relevant images. However, extracting the features of the images is one of the important steps. Recently Convolutional Neural Network (CNN) shows good results in the field of computer vision due to the ability of feature extraction from the images. Alex Net is a classical Deep CNN for image feature extraction. We have modified the Alex Net Architecture with a few changes and proposed a novel framework to improve its ability for feature extraction and for similarity measurement. The proposal approach optimizes Alex Net in the aspect of pooling layer. In particular, average pooling is replaced by max-avg pooling and the non-linear activation function Maxout is used after every Convolution layer for better feature extraction. This paper introduces CNN for features extraction from images in CBIR system and also presents Euclidean distance along with the Comprehensive Values for better results. The proposed framework goes beyond image retrieval, including the large-scale database. The performance of the proposed work is evaluated using precision. The proposed work show better results than existing works.

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Mobile Image Retrieval System for Cloud Service Based on Convolutional Neural Network and Hadoop

Lecture Notes in Computer Science - Artificial Intelligence and Security ◽

10.1007/978-3-030-24265-7_53 ◽

2019 ◽

pp. 617-626

Author(s):

Caijuan Huang ◽

Zhuohua Liu ◽

Hui Suo ◽

Bin Yang

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Retrieval System ◽

Cloud Service ◽

Image Retrieval System

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A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation

Volume 9: 40th Computers and Information in Engineering Conference (CIE) ◽

10.1115/detc2020-22048 ◽

2020 ◽

Author(s):

Shuo Jiang ◽

Jianxi Luo ◽

Guillermo Ruiz Pava ◽

Jie Hu ◽

Christopher L. Magee

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Visual Information ◽

Network Architecture ◽

Robot Arm ◽

Retrieval Method ◽

Patent Classification ◽

Patent Mining ◽

Design Retrieval

Abstract The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.

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A Novel Multi Hidden Layer Convolutional Neural Network for Content Based Image Retrieval

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4771.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 365-370

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Network Architecture ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Content Based Image Retrieval ◽

Neural Network Architecture ◽

Query Image

The applications of a content-based image retrieval system in fields such as multimedia, security, medicine, and entertainment, have been implemented on a huge real-time database by using a convolutional neural network architecture. In general, thus far, content-based image retrieval systems have been implemented with machine learning algorithms. A machine learning algorithm is applicable to a limited database because of the few feature extraction hidden layers between the input and the output layers. The proposed convolutional neural network architecture was successfully implemented using 128 convolutional layers, pooling layers, rectifier linear unit (ReLu), and fully connected layers. A convolutional neural network architecture yields better results of its ability to extract features from an image. The Euclidean distance metric is used for calculating the similarity between the query image and the database images. It is implemented using the COREL database. The proposed system is successfully evaluated using precision, recall, and F-score. The performance of the proposed method is evaluated using the precision and recall.

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