Image Retrieval Based on a Hybrid Model of Deep Convolutional Encoder

Qin, Jingkun; Haihong, E; Song, Meina; Ren, Zhijun

doi:10.1109/irce.2018.8492952

“…shows the overview look of our proposed convolutional neural network. It is very much similar to the other image recognition architectures[1,2,3,4] but has changed in the number of filters, neurons and activation functions for better performance. We can divide our model into six sequences of layers.…”

mentioning

confidence: 89%

“…Image recognition has an active community of academics studying it. A lot of important work on convolutional neural networks happened for image recognition [1,2,3,4]. The most dominant recent works achieved using CNN is a challenging work introduced by Alex Krizhevsky [5], who used CNN for challenge classification ImageNet.…”

Section: Related Workmentioning

confidence: 99%

Classification of Image using Convolutional Neural Network (CNN)

Hossain¹,

Sajib²

2019

GJCST

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Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

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“…Image recognition has an active community of academics studying it. A lot of important work on convolutional neural networks happened for image recognition [1,2,3,4]. The most dominant recent works achieved using CNN is a challenging work introduced by Alex Krizhevsky [5], who used CNN for challenge classification ImageNet.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Image using Convolutional Neural Network (CNN)

Sable

2024

IJRASET

0

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Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

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“…CNN is recognized as the second best error rate on the image classification task on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in 2012. Deep CNN is having hierarchies of feature representations in image classification tasks, where the semantic gap problem will be solved [16]. CNN is able to extract the significant image features in various layers, and allocate the image feature content into semantic concepts where the high-level of features descriptor will have good image depictions whereby it improves the image retrieval performance.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks in Image Retrieval System

Yogapriya*,

Dhivya,

Suvitha

2020

IJITEE

0

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Image is an important medium for monitoring the treatment responses of patient’s diseases by the physicians. There could be a tough task to organize and retrieve images in structured manner with respect to incredible increase of images in Hospitals. Text based image retrieval may prone to human error and may have large deviation across different images. Content-Based Medical Image Retrieval(CBMIR) system plays a major role to retrieve the required images from the huge database.Recent advances in Deep Learning (DL) have made greater achievements for solving complex problems in computer vision ,graphics and image processing. The deep architecture of Convolutional Neural Networks (CNN) can combine the low-level features into high-level features which could learn the semantic representation from images. Deep learning can help to extract, select and classify image features, measure the predictive target and gives prediction models to assist physician efficiently. The motivation of this paper is to provide the analysis of medical image retrieval system using CNN algorithm.

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Image Retrieval Based on a Hybrid Model of Deep Convolutional Encoder

Cited by 7 publications

References 6 publications

Classification of Image using Convolutional Neural Network (CNN)

Classification of Image using Convolutional Neural Network (CNN)

Classification of Image using Convolutional Neural Network (CNN)

Convolutional Neural Networks in Image Retrieval System

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