Content-Based Image Retrieval Using Convolutional Neural Networks

Ouhda, Mohamed; Asnaoui, Khalid El; Ouanan, Mohammed; Aksasse, Brahim

doi:10.1007/978-3-319-91337-7_41

Cited by 34 publications

(22 citation statements)

References 13 publications

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“…The table -IV shows a comparison of the average precision obtained using various experiments and with existing works. It is observed that the proposed work using SNN has performed better by 0.21% with average precision than the existing work [12].…”

Section: (A) Sample Output For Tiger Query (B) Sample Output For Rosementioning

confidence: 84%

“…The experiments have shown effective CBIR system with the combination of features than using individual features. Many recent works [8,9,10,11,12] have used the convolutional neural network for extraction of features from the images and store them. These methods have shown superior performances than the earlier methods of feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

2019

IJITEE

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With an advent of technologya huge collection of digital images is formed as repositories on world wide web (WWW). The task of searching for similar images in the repository is difficult. In this paper, retrieval of similar images from www is demonstrated with the help of combination of image features as color and shape and then using Siamese neural network which is constructed to the requirement as a novel approach. Here, one-shot learning technique is used to test the Siamese Neural Network model for retrieval performance. Various experiments are conducted with both the methods and results obtained are tabulated. The performance of the system is evaluated with precision parameter and which is found to be high.Also, relative study is made with existing works.

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Section: (A) Sample Output For Tiger Query (B) Sample Output For Rosementioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

2019

IJITEE

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“…Moreover, traditional CV techniques could not effectively handle selfies. This is attributed to two main reasons: (1) their big-data nature, where hand-crafted features are expensive to extract and might not generalize well in such volumes [35], (2) their non-standard capturing way, makes them always prone to extreme occlusion of facial landmarks. Moreover, these images might depict a side view of the face in addition to added emojis or artificial effects, for example, cartoon moustache.…”

Section: Selfie Imagesmentioning

confidence: 99%

A robust deep learning approach for glasses detection in non‐standard facial images

Bekhet

Alahmer

2020

IET Biometrics

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Automated glasses detection is a cardinal component in facial/ocular analysis that powers forensic, surveillance and biometric authentication systems. Throughout literature, glasses detection was always experimented by either utilizing hand‐crafted or deep learning features. Nevertheless, in both cases, highly standard face/ocular images were needed to derive the suggested technique. Both working methods performed reasonably well, but the results were bonded to the quality of the facial image and extracted features, where a slight shift and/or rotation in the input face image negatively affects the results. In addition, the obtained performance is even worse on real‐world (non‐standard) images, especially when compared to recent achievements in other computer vision research areas. In this paper, we present a robust deep learning approach for glasses detection from selfie photos full/partial frontal body non‐standard images captured in real‐life uncontrolled environments that do not utilize any facial landmarks. To the best of our knowledge this paper is the first to experiment detecting glasses from selfie photos, using a robust deep learning approach. Experimental results on various benchmark facial analysis datasets demonstrated the superior performance of the proposed technique with 96% accuracy.

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“…Mohamed, O. [7] , a paper related CBIR by CNN and SVM has been discussed for fast retrieval. More than 6000 images has been used for training from the ImageNet and Caltech256 database [7].…”

Section: Related Workmentioning

confidence: 99%

“…[7] , a paper related CBIR by CNN and SVM has been discussed for fast retrieval. More than 6000 images has been used for training from the ImageNet and Caltech256 database [7]. The classifier has reached the accuracy of 95% with SVM which would enable to use less images for training with error rate up to 11.9%.…”

Section: Related Workmentioning

confidence: 99%

An Insight Framework for Content based Medical Image Retrieval using Deep Convolutional Neural Network

Haripriya¹,

Porkodi²

2019

IJEAT

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Content Based Image Retrieval system is essentially proven techniques for retrieving the DICOM based images. DICOM format is use to acquire, store and shared medical images. Typically, retrieval of image under query upon the medical image databases is performed by fusing the low-level and high-level descriptors along with DCNN features. In this paper, DICOM image meta data are extracted by using dicom function and create the local database for reserve the information. To accomplish the initial search and retrieve the images by using extraction of Semantic information. The DICOM tags are extracted from the DICOM images and use DCNN feature to build a feature vector database. Subsequently prediction is done based on the by leveraging the convolution layers based on the meta data along with DCNN image features. This paper attempts to implement pre filter method to all DICOM images which further decrease the searching no of images, searching time and ultimately gives fast processing. DCNN based prediction model was constructed and finest results are accomplished. Average accuracy of precision and recall up to0.80 and 0.87 respectively is achieved based on precision value which would be suitable for high quality image retrieval based on semantic information confined in the image.

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Content-Based Image Retrieval Using Convolutional Neural Networks

Cited by 34 publications

References 13 publications

Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

A robust deep learning approach for glasses detection in non‐standard facial images

An Insight Framework for Content based Medical Image Retrieval using Deep Convolutional Neural Network

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