Content-Based Image Retrieval for Tourism Application

Raisi, Zobeir; Mohanna, Farahnaz; Rezaei, Mehdi

doi:10.1109/iranianmvip.2011.6121857

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…3.1 Hostel image database 3.1.1 Data sources. According to the relevant literature, the majority of relevant experiments used an existing database(s) (Abdullahzadeh and Mohanna, 2013;Wengert et al, 2011;Zhu et al, 2015), whereas some experiments used images specifically photographed for their research projects (Premchaiswadi et al, 2010;Raisi et al, 2011), and some experiments combined both data collection methods to create their image database (Raisi et al, 2014). Because of this, data collection methods are subjective.…”

Section: Methodsmentioning

confidence: 99%

Transfer learning for hostel image classification

Ammatmanee

Liu

2021

DTA

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PurposeBecause of the fast-growing digital image collections on online platforms and the transfer learning ability of deep learning technology, image classification could be improved and implemented for the hostel domain, which has complex clusters of image contents. This paper aims to test the potential of 11 pretrained convolutional neural network (CNN) with transfer learning for hostel image classification on the first hostel image database to advance the knowledge and fill the gap academically, as well as to suggest an alternative solution in optimal image classification with less labour cost and human errors to those who manage hostel image collections.Design/methodology/approachThe hostel image database is first created with data pre-processing steps, data selection and data augmentation. Then, the systematic and comprehensive investigation is divided into seven experiments to test 11 pretrained CNNs which transfer learning was applied and parameters were fine-tuned to match this newly created hostel image dataset. All experiments were conducted in Google Colaboratory environment using PyTorch.FindingsThe 7,350 hostel image database is created and labelled into seven classes. Furthermore, its experiment results highlight that DenseNet 121 and DenseNet 201 have the greatest potential for hostel image classification as they outperform other CNNs in terms of accuracy and training time.Originality/valueThe fact that there is no existing academic work dedicating to test pretrained CNNs with transfer learning for hostel image classification and no existing hostel image-only database have made this paper a novel contribution.

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Section: Methodsmentioning

confidence: 99%

Transfer learning for hostel image classification

Ammatmanee

Liu

2021

DTA

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“…The existing studies mainly aim to improve the description ability of feature descriptor and the matching efficiency of salient features [10][11][12]. Beaudoin, [13] performed template matching between the target images and a landmark image database, and obtained a descriptor for long-distance visual features of images similar to the templates.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Multiple Spatiotemporally Correlated Images on Tourist Attractions Based on Image Processing

Lü¹,

Zhang²,

Liu³

et al. 2020

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The thriving of information technology (IT) has elevated the demand for intelligent query and retrieval of information about the tourist attractions of interest, which are the bases for preparing convenient and personalized itineraries. To realize accurate and rapid query of tourist attraction information (not limited to text information), this paper proposes a spatiotemporal feature extraction method and a ranking and retrieval method for multiple spatiotemporally correlated images (MSCIs) on tourist attractions based on deeply recursive convolutional network (DRCN). Firstly, the authors introduced the acquisition process of candidate spatiotemporally correlated images on tourist attractions, including both coarse screening and fine screening. Next, the workflow of spatiotemporal feature extraction from tourist attraction images was explained, as well as he proposed convolutional long short-term memory (ConvLSTM) algorithm. After that, the ranking model of MSCIs was constructed and derived. Experimental results demonstrate that our strategy is effective in the retrieval of tourist attraction images. The research results shed light on the fast and accurate retrieval of other types of images.

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“…In this method, first the differential and robust current are calculated and the main frequency of each current is compared with the others in order to detect the phase angle difference (PAD) or the secondary currents of the related transformer. Another method is based on wavelet transform, neural networks or integrated wavelet transform based on support vector machine [7], [20]. In the other technique, the mean absolute deviation (MAD) and the wavelet coefficients on a specific frequency band of wavelet transform are used [8], [21].…”

Section: Introductionmentioning

confidence: 99%

The Power Transformer Differential Protection Using Decision Tree

Azarakhsh¹

2017

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This paper presents a new power transformer protection method by intelligent differential relay. This method is based on differential current sampling caused by primary and secondary three phase current of transformer. Detecting the type of fault in protection place is based on performance of the learned decision tree. The simulations of power system and differential relay are accomplished in PSCAD and MATLAB. The main characteristic of the relay is being intelligence such that at first step the decision tree is learned by several samples including faulted and inrush currents and then the relay detects the internal fault intelligently and quickly and the outputs of the relay order to the breakers in two ends of the transformer to be opened.

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Content-Based Image Retrieval for Tourism Application

Cited by 5 publications

References 18 publications

Transfer learning for hostel image classification

Transfer learning for hostel image classification

Retrieval of Multiple Spatiotemporally Correlated Images on Tourist Attractions Based on Image Processing

The Power Transformer Differential Protection Using Decision Tree

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