Nus-Wide

Chua, Tat-Seng; Tang, Jinhui; Hong, Richang; Li, Haojie; Luo, Zhiping; Yong, Zheng

doi:10.1145/1646396.1646452

Cited by 2,312 publications

(117 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This data set includes 269 648 images and associated tags crawled from the popular social forum Flickr [9]. The images are labeled over 81 concepts which can be used as ground truths for performance evaluation, although some images still do not have complete labels over the categories.…”

Section: B Nus-wide Data Setmentioning

confidence: 99%

Robust and Scalable Graph-Based Semisupervised Learning

2012

View full text Add to dashboard Cite

Graph-based semisupervised learning methods and new techniques for handling contaminated noisy labels, and gigantic data sizes for web applications, are reviewed in this paper.By Wei Liu, Jun Wang, Member IEEE, and Shih-Fu Chang, Fellow IEEE ABSTRACT | Graph-based semisupervised learning (GSSL) provides a promising paradigm for modeling the manifold structures that may exist in massive data sources in highdimensional spaces. It has been shown effective in propagating a limited amount of initial labels to a large amount of unlabeled data, matching the needs of many emerging applications such as image annotation and information retrieval. In this paper, we provide reviews of several classical GSSL methods and a few promising methods in handling challenging issues often encountered in web-scale applications. First, to successfully incorporate the contaminated noisy labels associated with web data, label diagnosis and tuning techniques applied to GSSL are surveyed. Second, to support scalability to the gigantic scale (millions or billions of samples), recent solutions based on anchor graphs are reviewed. To help researchers pursue new ideas in this area, we also summarize a few popular data sets and software tools publicly available. Important open issues are discussed at the end to stimulate future research.

show abstract

Section: B Nus-wide Data Setmentioning

confidence: 99%

Robust and Scalable Graph-Based Semisupervised Learning

2012

View full text Add to dashboard Cite

show abstract

“…Among the proposed methods, Rough set theory has the ability to cluster profitably data that comes from image analysis [16] as efficiently identify the edge that is one of the effective methods of segmentation, convergence time So we proposed this theory we use for segmentation of images. Rough set more detailed description about the image segmentation in the paper [17] can see. After extracting the piece of the picture, we produce dense piece band, we take action the k-means clustering pieces by the charges.…”

Section: A Image Segmentation In Every Particular Classmentioning

confidence: 99%

Automatic Image Annotation based on Dense Weighted Regional Graph

Boorjandi¹,

Ghobadi²,

Rashidi³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Automatic image annotation refers to create text labels in accordance with images' context automatically. Although, numerous studies have been conducted in this area for the past decade, existence of multiple labels and semantic gap between these labels and visual low-level features reduced its performance accuracy. In this paper, we suggested an annotation method, based on dense weighted regional graph. In this method, clustering areas was done by forming a dense regional graph of area classification based on strong fuzzy feature vector in images with great precision, as by weighting edges in the graph, less important areas are removed over time and thus semantic gap between low-level features of image and human interpretation of high-level concepts reduces much more. To evaluate the proposed method, COREL database, with 5,000 samples have been used. The results of the images in this database, show acceptable performance of the proposed method in comparison to other methods.

show abstract

“…The texture feature vector of an image is computed with both pyramid-and tree-structured wavelet transform by decomposing, at different levels, the sub-bands obtained through filtering. And it consists of the means and standard deviations of all the energy distributions [3]. As for SIFT, a visual vocabulary of size 500 is constructed through k-means clustering.…”

Section: Image Representation and Similarity Measurementmentioning

confidence: 99%

“…p(z j |I i ), which can be decomposed by considering all the images similar to the given one as in (3).…”

Section: Image-to-topic Relevance Relationshipmentioning

confidence: 99%

See 1 more Smart Citation

High-level semantic image annotation based on hot Internet topics

Wang

et al. 2013

Multimed Tools Appl

View full text Add to dashboard Cite

Images are complex multimedia data that contain rich semantic information. Currently, most of image annotation algorithms are only annotating the object semantics of images. There are still many challenges on high-level semantic image annotation. The major issues are the lack of effective modeling method for the highlevel semantics of images and the lack of efficient dynamic update mechanism for the training set. To address these issues, we propose a high-level semantic annotation method based on hot Internet topics in this paper. There are two independent sub tasks in our method: dynamic update of the training set based on hot Internet topics and search-based image annotation. In the first sub task, we propose to model the abstract semantics of images based on three relationships: image-to-image similarity relationship, topic-to-topic co-occurrence relationship, and image-to-topic relevance relationship. Through the complex graph clustering, the hot Internet topics are extracted for images with consistent visual and semantic contents. Then the dynamic update mechanism will update the original training set with the new topics and images. It avoids the huge computing cost in traditional update methods and does not need to re-calculate the whole mapping relationship between the semantic concepts and visual features. In the second sub task, given a query image, it first searches for similar candidates in the annotated training set via visual features. Then the hypergraph modeling and spectral clustering are exploited to filter out the images with irrelevant semantics. The keywords will be extracted for annotation from the remaining images according to an annotation probability. Extensive experiments have been conducted and the results demonstrate that our algorithm could achieve better annotation performance than the state-of-the-art algorithms. And the update mechanism could extend the training set efficiently so that the coverage of the semantics in the training set wouldn't be obsolete.

show abstract

Nus-Wide

Cited by 2,312 publications

References 18 publications

Robust and Scalable Graph-Based Semisupervised Learning

Robust and Scalable Graph-Based Semisupervised Learning

Automatic Image Annotation based on Dense Weighted Regional Graph

High-level semantic image annotation based on hot Internet topics

Contact Info

Product

Resources

About