Content-based image retrieval using Group Normalized-Inception-Darknet-53

Pathak, Debanjan; Raju, U. S. N.

doi:10.1007/s13735-021-00215-4

Cited by 9 publications

(4 citation statements)

References 58 publications

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“…The backbone of the YOLOv3 algorithm is Darknet-53 [ 19 ], which has a small shallow feature grid area during feature extraction and provides output for location information, most notably 13 × 13, 26 × 26, and 52 × 52 fusion methods, resulting in a minimum resolution scale feature of 52 × 52 size.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Detection Algorithm of Flame Target Image

Jing

2022

Computational Intelligence and Neuroscience

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In many research tasks, the speed and accuracy of flame detection using supply chain have always been a challenging task for many researchers, especially for flame detection of small objects in supply chain. In view of this, we propose a new real-time target detection algorithm. The first step is to enhance the flame recognition of small objects by strengthening the feature extraction ability of multi-scale fusion. The second step is to introduce the K-means clustering method into the prior bounding box of the algorithm to improve the accuracy of the algorithm. The third step is to use the flame characteristics in YOLO+ algorithm to reject the wrong detection results and increase the detection effect of the algorithm. Compared with the YOLO series algorithms, the accuracy of YOLO+ algorithm is 99.5%, the omission rate is 1.3%, and the detection speed is 72 frames/SEC. It has good performance and is suitable for flame detection tasks.

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Section: Related Workmentioning

confidence: 99%

A Real-Time Detection Algorithm of Flame Target Image

Jing

2022

Computational Intelligence and Neuroscience

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“…After training the CNN-based models, all models were tested to classify images using the validation data set. Consequently, the top 5 CNN-based models were selected based on accuracy to apply them to FACES (Figure 1c) as follows: ResNet-101 [34] (accuracy: 90.75%), DarkNet-19 [35] (90.25%), DarkNet-53 [36] (89.5%), ResNet-50 [37] (89.0%), and GoogleNet [38] (88.5%) (Table 2).…”

Section: Methodology For Skin Rosacea Detection Combining the Five Be...mentioning

confidence: 99%

FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses

et al. 2023

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Rosacea is a chronic inflammatory skin disorder that causes visible blood vessels and redness on the nose, chin, cheeks, and forehead. However, visual assessment, the current standard method used to identify rosacea, is often subjective among clinicians and results in high variation. Recent advances in artificial intelligence have allowed for the effective detection of various skin diseases with high accuracy and consistency. In this study, we develop a new methodology, coined “five accurate CNNs-based evaluation system (FACES)”, to identify and classify rosacea more efficiently. First, 19 CNN-based models that have been widely used for image classification were trained and tested via training and validation data sets. Next, the five best performing models were selected based on accuracy, which served as a weight value for FACES. At the same time, we also applied a majority rule to five selected models to detect rosacea. The results exhibited that the performance of FACES was superior to that of the five individual CNN-based models and the majority rule in terms of accuracy, sensitivity, specificity, and precision. In particular, the accuracy and sensitivity of FACES were the highest, and the specificity and precision were higher than most of the individual models. To improve the performance of our system, future studies must consider patient details, such as age, gender, and race, and perform comparison tests between our model system and clinicians.

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“…The primary benefit of a very deep network is its capacity to represent highly complex functions. For example, an image can be taught characteristics at a variety of layers of abstraction, from edges at the lower layers to more complex features [9] at the higher layers. Using a deeper network, on the other hand has some issues.…”

Section: Segmentationmentioning

confidence: 99%

Image Retrieval Using Auto Encoding Features In Deep Learning

Syed Qamrul Kazmi

2023

IJRITCC

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The latest technologies and growth in availability of image storage in day to day life has made a vast storage place for the images in the database. Several devices which help in capturing the image contribute to a huge repository of images. Keeping in mind the daily input in the database, one must think of retrieving those images according to certain criteria mentioned. Several techniques such as shape of the object, Discrete Wavelet transform (DWT), texture features etc. were used in determining the type of image and classifying them. Segmentation also plays a vital role in image retrieval but the robustness is lacking in most of the cases. The process of retrieval mainly depends on the special characteristics possessed by an image rather than the whole image. Two types of image retrieval can be seen. One with a general object and the other which may be specific to some type of application. Modern deep neural networks for unsupervised feature learning like Deep Autoencoder (AE) learn embedded representations by stacking layers on top of each other. These learnt embedded-representations, however, may degrade as the AE network deepens due to vanishing gradient, resulting in decreased performance. We have introduced here the ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature based learning. The proposed model is tested on three distinct databases Corel1K, Cifar-10, Cifar-100 which differ in size. The presented algorithm have significantly reduced computation time and provided very high image retrieval levels of accuracy.

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Content-based image retrieval using Group Normalized-Inception-Darknet-53

Cited by 9 publications

References 58 publications

A Real-Time Detection Algorithm of Flame Target Image

A Real-Time Detection Algorithm of Flame Target Image

FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses

Image Retrieval Using Auto Encoding Features In Deep Learning

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