A robust CBIR framework in between bags of visual words and phrases models for specific image datasets

Ouni, Achref; Urruty, Thierry; Visani, Muriel

doi:10.1007/s11042-018-5841-8

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…We consider the vector length of the proposed methods is 64 if during the build-ing of the signature the descriptor used is SURF or KAZE. For VLAD [17], N-BoVW [30], BoVW [9] the length of the vector depends on the number K which is used to calculate the visual words using the K-means algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Leveraging semantic segmentation for hybrid image retrieval methods

Ouni

Royer

Chevaldonné

et al. 2021

Neural Comput & Applic

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Content Based Image Retrieval (CBIR) is the task of finding images in a database that are the most similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. In this work, we propose three different methodologies combining deep learning based semantic segmentation and visual features. We show experimentally that by exploiting semantic information in the CBIR context leads to an increase in the retrieval accuracy. We study the performance of the proposed approach on eight different datasets (Wang, Corel-10k, Corel-5k, GHIM-10K, MSRC V1, MSRC V2, Linnaeus, NUS-WIDE)

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Section: Methodsmentioning

confidence: 99%

“…The authors in [76], propose a robust and invariant descriptor to rotation and illumination. Another method inspired by BoVW is the Bag of visual phrase (BoVP) [30,2,31]. BoVP describe the image as a matrix of visual phrase occurrence instead of a vector in BoVW.…”

Section: Fig 2 Bag Of Visual Words Modelmentioning

confidence: 99%

Leveraging semantic segmentation for hybrid image retrieval methods

Ouni

Royer

Chevaldonné

et al. 2021

Neural Comput & Applic

Self Cite

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show abstract

“…MSRC v1 MSRC v2 Linnaeus Wang BoVW [8] 0,48 0.30 0,26 0.48 n-BoVW [17] 0.58 0.39 0.31 0.60 VLAD [10] 0.78 0.41 -0.74 N-Gram [18] ---0.37 AlexNet [12] 0.81 0.58 0,47 0.68 VGGNet [23] 0.76 0.63 0,48 0.76 ResNet [25] 0.83 0.70 0,69 0.82 Ruigang [22] --0.70 -Ours (best) 0.86 0.72 0.75 0.84 Table 6. Comparison of the accuracy of our approach with methods from the state of the art…”

Section: Methodsmentioning

confidence: 99%

“…VLAD and Fisher are similar but VLAD does not store second order information about the features and use K-MEANS instead GMM. Another inspiration from BoVW presented by Bag of visual phrase (BoVP) [17] [2] [18]. BoVP describe the image as a matrix of visual phrase occurrence instead of a vector in BoVW.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

Ouni

Royer

Chevaldonné

et al. 2020

Advances in Visual Computing

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“…Visual phrase model is proposed to overcome the high-dimensional and quantization errors of the bag of visual words (BovW) model. Based on the well-known BovW model, Ouni et al propose three different methodologies [11] inspired by the visual phrase model effectiveness and a compression technique which ensures the same effectiveness for retrieval than the BoVW model. Due to the large-scale use of neural networks in the field of image classification and recognition, different types of deep neural networks are proposed for image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

et al. 2020

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The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.

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A robust CBIR framework in between bags of visual words and phrases models for specific image datasets

Cited by 7 publications

References 26 publications

Leveraging semantic segmentation for hybrid image retrieval methods

Leveraging semantic segmentation for hybrid image retrieval methods

A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

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