Semantic content based image retrieval technique using cloud computing

Karande, Supriya Jalindar; Maral, Vikas

doi:10.1109/iccic.2013.6724277

Cited by 3 publications

(1 citation statement)

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“…(Krizhevsky et al, 2017) has used convolution neural network consisting of five convolution layers and pooling layers having 60 million parameters and 650,000 neurons to classify the 1.2 million highresolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. (Karande and Maral, 2013) have shown relevance feedback technique using artificial neural network trained feature vectors obtained from HSV model and texture to reduce the semantic though used the cloud computing to meet the challenge of computing power. (Wan et al, 2014) have investigated towards the effective role of deep learning in reducing the semantic gap their empirical study on Caltech256 dataset has revealed that pre-trained (convolutional neural networks) CNN model on large scale dataset are able to capture high semantic information in the raw pixels and can be directly used for features extraction in CBIR tasks.…”

Section: Introductionmentioning

confidence: 99%

Semantic Based Image Retrieval Through Combined Classi?ers of Deep Neural Network and Wavelet Decomposition of Image Signal

Qazi¹,

Wong²

2018

Linköping Electronic Conference Proceedings

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Semantic gap, high retrieval efficiency, and speed are important factors for content-based image retrieval system (CBIR). Recent research towards semantic gap reduction to improve the retrieval accuracy of CBIR is shifting towards machine learning methods, relevance feedback, object ontology etc. In this research study, we have put forward the idea that semantic gap can be reduced to improve the performance accuracy of image retrieval through a two-step process. It should be initiated with the identification of the semantic category of the query image in the first step, followed by retrieving of similar images from the identified semantic category in the second step. We have later demonstrated this idea through constructing a global feature vector using wavelet decomposition of color and texture information of the query image and then used feature vector to identify its semantic category. We have trained a stacked classifier consisting of deep neural network and logistic regression as base classifiers for identifying the semantic category of input image. The image retrieval process in the identified semantic category was achieved through gabor filter of the texture information of query image. This proposed algorithm has shown better precision rate of image retrieval than that of other researchers work

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Section: Introductionmentioning

confidence: 99%