MultiVCRank With Applications to Image Retrieval

Li, Xutao; Ye, Yunming; Ng, Michael K.

doi:10.1109/tip.2016.2522298

Cited by 11 publications

(5 citation statements)

References 43 publications

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“…Li et al [12] proposed a MutiVCRank with Application to Image Retrieval. In this method features are extracted from images using Scale Invariant Feature Transform (SIFT) and regions are detected with the help of salient region detection method.…”

Section: B Hypergraph Based Methodsmentioning

confidence: 99%

Image Retrieval using Hypergraph of Visual Concepts

Kawale¹,

Kamalapur²

2017

IJITCS

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Abstract-Retrieving images similar to query image from a large image collection is a challenging task. Image retrieval is most useful in the image search engine to find images similar to the query image. Most of the existing graph based image retrieval methods capture only pairwise similarity between images. The proposed work uses the hypergraph approach of the visual concepts. Each image can be represented by combination of the several visual concepts. Visual concept is the specific object or part of an image. There are several images in the database which can share multiple visual concepts. To capture such a relationship between group of images hypergraph is used. In proposed work, each image is considered as a vertex and each visual concept as a hyperedge in a hypergraph. All the images sharing same visual concept, form a hyperedge. Images in the dataset are represented using hypergraph. For each query image visual concept is identified. Similarity between query image and database image is identified. According to these similarities association scores are assigned to images, which will handle the image retrieval.

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Section: B Hypergraph Based Methodsmentioning

confidence: 99%

Image Retrieval using Hypergraph of Visual Concepts

Kawale¹,

Kamalapur²

2017

IJITCS

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“…Methods. e effectiveness of the WTCPN method is analyzed experimentally on two public datasets, specifically Oxford [27] and Holiday [28]. Oxford architecture dataset contains 55 query images corresponding to 11 different buildings.…”

Section: Common Datasets and Performance Evaluationmentioning

confidence: 99%

Intelligent Retrieval Method of Approximate Painting in Digital Art Field

Wan¹,

Yu²

2021

Scientific Programming

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With the rapid development of Internet technology and the wide application of image acquisition equipment, the number of digital artwork images is exploding. The retrieval of near-similar artwork images has a wide application prospect for copyright infringement, trademark registration, and other scenes. However, compared with traditional images, these artwork images have the characteristics of high similarity and complexity, which lead to the retrieval accuracy not meeting the demand. To solve the above problems, an intelligent retrieval method of artwork image based on wavelet transform and dual propagation neural network (WTCPN) is proposed. Firstly, the original artwork image is replaced by the low-frequency subimage after wavelet transform, which not only removes redundant information and reduces the dimension of data but also suppresses random noise. Secondly, in order to make the network assign different competition winning units to different types of modes, the dual propagation neural network is improved by setting the maximum number of times of winning neurons. Experimental results show that the proposed method can improve the accuracy of image retrieval, and the recognition accuracy of verification set can reach over 91%.

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“…Other improvements in the analysis of important components in multi-dimensional data are based on tensor factorization; see, e.g., [18,26,30]. In [21] a tensor-based ranking scheme is applied to hypergraphs in order to develop a multi-visual-concept ranking scheme for image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS

Arrigo¹,

Tudisco²

2019

Proceedings of the 2019 SIAM International Conference on Data Mining

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We introduce a ranking model for temporal multidimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous orderpreserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm.

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MultiVCRank With Applications to Image Retrieval

Cited by 11 publications

References 43 publications

Image Retrieval using Hypergraph of Visual Concepts

Image Retrieval using Hypergraph of Visual Concepts

Intelligent Retrieval Method of Approximate Painting in Digital Art Field

Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS

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