Content-based Image Retrieval based on Convolutional Neural Networks

Ghaleb, Moshira S.; Ebied, Hala M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1109/icicis52592.2021.9694146

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The system demonstrated an overall F-measure of 0.85 for natural language-based retrieval. The paper concludes with insights into the retrieval results and suggestions for further research [14].…”

Section: Discrete Cosine Transformmentioning

confidence: 97%

Content Based Video Retrieval using Deep Learning

Palanivelu,

Jagdale,

Dubey

et al. 2024

Preprint

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Content-based video retrieval (CBVR) is part of a search engine which is widely utilized in everyday life for retrieving relevant images/frames from given large datasets. Generally, the existing methods don't give the relevance and expected images as per the query images and also require more computational time. The CBVR systems depend on the feature representation and similarity measurements. In this project, a CBVR system is developed using deep learning models. The convolutional neural network (CNN) is used to extract features from an input image/frame, and then comparing those features with images in datasets to retrieve similar images/frames. The process involves three main phases namely feature extraction, feature comparison, and retrieval. In feature extraction, the CNN is trained on a dataset of images to learn to extract features that are relevant to the task at hand. In feature comparison, the features extracted from the input image are compared to the features of the images in the database using a similarity measure such as Euclidean distance. Finally, in the retrieval phase, the most similar images/frames from the datasets are returned as the results. The CBVR is carried out using a pre-trained deep learning model, such as VGG16 or ResNet, which is used to train over large image datasets and can extract useful features from new images. In this project, the retrieval of images are carried out and the performance is evaluated based on accuracy, precision, recall. Various CCN models are experimented to perform result analysis. Keywords: Control based video retrieval, Deep learning, Convolutional Neural Network, Information Retrieval, Feature extraction, Inception V3, Feature selection

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Section: Discrete Cosine Transformmentioning

confidence: 97%

Content Based Video Retrieval using Deep Learning

Palanivelu,

Jagdale,

Dubey

et al. 2024

Preprint

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“…Although current deep unfolding methods have improved the interpretability of deep unfolding, their performance still lags behind methods based on deep unfolding [21]. Image signals possess an essential influence on traditional image processing applications, and the reconstruction ability of signals can be reconstructed through a good dictionary.…”

Section: The Image Adaptive Denoising Modelmentioning

confidence: 99%

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

2024

Teh. vjesn.

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Image denoising is essential for numerous image processing applications, where image noise can profoundly impact processing efficiency and output quality. Addressing the challenge of inflexible reference images in unconditional diffusion probability models and enhancing image denoising performance is of paramount importance. In this research, we propose a novel image denoising model based on component decoupling and introduce sensitivity decoupling operators to prevent entanglement and redundancy among different decoupling models. Additionally, we leverage a model-driven network to fuse image components, resisting noise and model degradation, thereby aiding network convergence. Subsequently, we construct an image adaptive denoising model incorporating diffusion probability and dictionary learning. Experimental results demonstrate the superiority of the proposed approach over other algorithms in grayscale image processing on the Set12 dataset, achieving a peak signal-to-noise ratio (PSNR) of 35.75 dB and an average structural similarity (SSIM) value of 92.68%. Similarly, on the BSD68 dataset, our algorithm outperforms others with a PSNR of 34.35 dB and an average SSIM of 93.89%. Furthermore, for colour image processing, our method yields higher PSNR and average SSIM compared to other approaches. The findings indicate a significant improvement in denoising effectiveness compared to prior methods, highlighting the practical value of the proposed image denoising algorithm.

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