Automatic image annotation using community detection in neighbor images

Maihami, Vafa

doi:10.1016/j.physa.2018.05.028

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…The tag graph is created from tags associated with visually similar images. In [28], the tags are recommended for the images using the user's history based on majority voting from the neighbors by creating a graph that captures the relationship between the user, image features, and ranked tags. The tag relevance method is presented in [29] based on weighted visual neighbors.…”

Section: ) Neighbor Based Methodsmentioning

confidence: 99%

Image Tagging using Modified Association Rule based on Semantic Neighbors

Phakatkar,

Sonkamble

2023

IJRITCC

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With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images. Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value. The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods.

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Section: ) Neighbor Based Methodsmentioning

confidence: 99%

Image Tagging using Modified Association Rule based on Semantic Neighbors

Phakatkar,

Sonkamble

2023

IJRITCC

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“…The method was proposed in [12] for annotation of images using a variation of traditional kNN algorithm by defining matrix which shows the relationship between labels and images. In [13] the method was proposed in which the given an image the similar images were determined using k nearest neighbor and tag graph was created from tags of neighbors and clustered to assign label to an image. The personalized image tag recommendation method was proposed based on neighbor voting scheme by building tripartite graph to show relationship between user, tags and images in [14].…”

Section: Work Donementioning

confidence: 99%

Image Tag Recommendation based on Ranked Categorical Nearest Neighbors and Weighted Tag Features

Dondekar¹,

Sonkamble²

2020

Adv. sci. technol. eng. syst. j.

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“…AIA is the process that the computer automatically provides many keywords/labels/tags which reflect the visual content of an image. A host of methods [3,4,8,10,[20][21][22]25,30,34,35] are proposed to resolve the problem of image annotation. They are divided into two categories: model without graph structure and model with graph structure.…”

Section: Introductionmentioning

confidence: 99%

Multiple Graphs Structure of Intra-class Representation with Semantic Information for Image Annotation

liu,

Liu,

Shao

et al. 2023

Preprint

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Automatic image annotation has absorbed a few research interests in recent decades quietly. Nevertheless, most methods leave graph structure out of consideration or utilize merely the graph without semantic information to capture manifold information of samples. Furthermore, it is inaccurate to consider the label matrix of a dataset as the feature matrix of tags during the computing label graph. To overcome these shortcomings, we propose a method, called Multiple Graphs Structure of Intra-class Representation with Semantic Information for Image Annotation. Our method proposes a superior multiple graphs structure called multiple graphs structure of intra-class representation as combining the semantic information with sample graph structure. Then, the probability Laplacian graph is presented to obtain the relationship between tags according to prior knowledge instead of exploiting tag features. Afterward, some experiments have been implemented to certify the effectiveness of the proposed graph structures on Corel5K, ESPGame, IAPRTC12, and NUS-WIDE datasets.

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Automatic image annotation using community detection in neighbor images

Cited by 16 publications

References 32 publications

Image Tagging using Modified Association Rule based on Semantic Neighbors

Image Tagging using Modified Association Rule based on Semantic Neighbors

Image Tag Recommendation based on Ranked Categorical Nearest Neighbors and Weighted Tag Features

Multiple Graphs Structure of Intra-class Representation with Semantic Information for Image Annotation

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