Asynchronous Bi-clustering for Image Recommendation

2018 ◽  
pp. 411-426
Author(s):  
Yankun Jia ◽  
Yidong Li ◽  
Jun Wu
Keyword(s):  
Image Recommendation

Social Image Recommendation Based on Path Relevance

2018 ◽  
pp. 165-180 ◽  
Author(s):  
Zhang Chuanyan ◽  
Hong Xiaoguang ◽  
Peng Zhaohui
Keyword(s):  
Social Image ◽  
Image Recommendation

JustClick: Personalized Image Recommendation via Exploratory Search From Large-Scale Flickr Images

2009 ◽  
Vol 19 (2) ◽  
pp. 273-288 ◽  
Author(s):  
Jianping Fan ◽  
D.A. Keim ◽  
Yuli Gao ◽  
Hangzai Luo ◽  
Zongmin Li
Keyword(s):  
Large Scale ◽  
Exploratory Search ◽  
Image Recommendation ◽  
Flickr Images

scholarly journals Image recommendation algorithm based on profile using user preference and visual descriptor

2008 ◽  
Vol 15D (4) ◽  
pp. 463-474
Author(s):  
Deok-Hwan Kim ◽  
Jun-Sik Yang ◽  
Won-Hee Cho
Keyword(s):  
User Preference ◽  
Recommendation Algorithm ◽  
Image Recommendation

scholarly journals Picture semantic similarity search based on bipartite network of picture-tag type

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259028
Author(s):  
Mingxi Zhang ◽  
Liuqian Yang ◽  
Yipeng Dong ◽  
Jinhua Wang ◽  
Qinghan Zhang
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Semantic Similarity ◽  
Similarity Search ◽  
Recommendation System ◽  
Search Method ◽  
Visual Features ◽  
Bipartite Network ◽  
Effectiveness And Efficiency ◽  
Image Recommendation

Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by “description” relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that “similar pictures contain similar tags, and similar tags describe similar pictures”, which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.

scholarly journals Image Recommendation Algorithm Based on Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132799-132807
Author(s):  
Pei Yin ◽  
Liang Zhang
Keyword(s):  
Deep Learning ◽  
Recommendation Algorithm ◽  
Image Recommendation

The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching

2021 ◽  
Vol 20 (6) ◽  
pp. 1-17
Author(s):  
Yaolin Tian ◽  
Weize Gao ◽  
Xuxing Liu ◽  
Shanxiong Chen ◽  
Bofeng Mo
Keyword(s):  
Good Effect ◽  
Feature Descriptor ◽  
Curve Matching ◽  
Accuracy Rate ◽  
Search Range ◽  
Average Accuracy ◽  
Image Recommendation ◽  
Accuracy Of Results ◽  
Detail Loss ◽  
Target Matching

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.

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