Two-stage content based image retrieval using sparse representation and feature fusion

Wang, Wen Qing; Jiao, Pengfei; Liu, Ran; Ma, Xiao; Shang, Zhuo

doi:10.1007/s11042-022-12348-7

Cited by 16 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the improvement of the computing ability of hardware platforms, the introduction of excellent networks such as AlexNet (Krizhevsky et al, 2017) in 2012, and an ever-expanding database of digital images (Wang et al, 2022b), deep learning methods have been widely used in image processing (Li et al, 2017;Cao et al, 2018;Hou et al, 2018), natural language analysis (dos Santos and Gatti, 2014), and speech processing (Bollepalli et al, 2017). Datadriven deep learning-based methods are end-to-end methods, and they can solve the above problems easily by making the model learn basic parameters directly from the input.…”

Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

An underwater image enhancement model for domain adaptation

Deng

Liu

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM.

show abstract

Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

An underwater image enhancement model for domain adaptation

Deng

Liu

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…CBMIR has been a subject of extensive research since the advent of large-scale databases nearly two decades ago, as noted by Wang [14]. Several studies have made significant contributions to this field.…”

Section: Content-based Medical Image Retrieval (Cbmir)mentioning

confidence: 99%

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Tabatabaei,

Wang,

Colomer

et al. 2023

Bioengineering

View full text Add to dashboard Cite

The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training.

show abstract

“…Then an incremental nearest neighbour (NN) selection is used to implement k-NN for dynamic query selection. Wang, W., et al [26] presented a two-stage CBIR model using the fusion of global and local feature. Authors use a sparse coding for the sparse representation of the local features followed by feature www.ijacsa.thesai.org pooling and the Euclidean distance measure is used to find the similarity between the sparse feature vectors.…”

Section: IImentioning

confidence: 99%

“…It also helps to maintain a distinctive signature for the images of different classes. In recent years image retrieval using feature fusion has been emphasized by many researchers [3,8] to build a more powerful image descriptor using the feature fusion technique [7,10,23,[25][26][27]. These are more sensitive to noise and image resolution.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Palai¹,

Jena²,

Pattanaik³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Recent development of digital photography and the use of social media using smartphones has boosted the demand for image query by its visual semantics. Content-Based Image Retrieval (CBIR) is a well-identified research area in the domain of image and video data analysis. The major challenges of a CBIR system are (a) to derive the visual semantics of the query image and (b) to find all the similar images from the repository. The objective of this paper is to precisely define the visual semantics using hybrid feature vectors. In this paper, a CBIR system using encoded-based feature fusion is proposed. The CNN encoding features of the RGB channel are fused with the encoded texture features of LBP, CSLBP, and LDP separately. The retrieval performance of the different fused features is tested using three public datasets i.e. Corel-lK, Caltech, and 102flower. The result shows the class properties are better retained using the LDP with RGB encoded features, this helps to enhance the classification and retrieval performance for all three datasets. The average precision of Corel-lK is 94.5% and it is 89.7% for Caltech, and 88.7% for the 102flower. The average f1-score is 89.5% for Caltech, and 88.5% for the 102flower. The improvement in the f1-score value implies the proposed fused feature is more stable to deal the class imbalance problem.

show abstract

Two-stage content based image retrieval using sparse representation and feature fusion

Cited by 16 publications

References 46 publications

An underwater image enhancement model for domain adaptation

An underwater image enhancement model for domain adaptation

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Contact Info

Product

Resources

About