Screen3D: A Novel Fully Flexible High-Throughput Shape-Similarity Search Method

2014 ◽  
Vol 54 (4) ◽  
pp. 1036-1049 ◽  
Author(s):  
Adrián Kalászi ◽  
Dániel Szisz ◽  
Gábor Imre ◽  
Tímea Polgár
Keyword(s):  
High Throughput ◽  
Similarity Search ◽  
Search Method ◽  
Shape Similarity

A multistep approach for shape similarity search in image databases

1998 ◽  
Vol 10 (6) ◽  
pp. 996-1004 ◽  
Author(s):  
M. Ankerst ◽  
H.-P. Kriegel ◽  
T. Seidl
Keyword(s):  
Similarity Search ◽  
Image Databases ◽  
Shape Similarity

An efficient and effective video similarity search method

2011 ◽  
Author(s):  
Liuzhang Zhu ◽  
Zimian Li ◽  
Zheng Cao
Keyword(s):  
Similarity Search ◽  
Search Method

scholarly journals Omokage Search and Gmfit: Shape Similarity Search and Superposition among Models and Maps

2016 ◽  
Vol 110 (3) ◽  
pp. 157a
Author(s):  
Takeshi Kawabata ◽  
Hirofumi Suzuki ◽  
Haruki Nakamura
Keyword(s):  
Similarity Search ◽  
Shape Similarity

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.

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