Fang-Fei Kuo

418 total citations
12 papers, 271 citations indexed

About

Fang-Fei Kuo is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Information Systems. According to data from OpenAlex, Fang-Fei Kuo has authored 12 papers receiving a total of 271 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 9 papers in Signal Processing and 4 papers in Information Systems. Recurrent topics in Fang-Fei Kuo's work include Music and Audio Processing (8 papers), Music Technology and Sound Studies (4 papers) and Video Analysis and Summarization (4 papers). Fang-Fei Kuo is often cited by papers focused on Music and Audio Processing (8 papers), Music Technology and Sound Studies (4 papers) and Video Analysis and Summarization (4 papers). Fang-Fei Kuo collaborates with scholars based in Taiwan and United States. Fang-Fei Kuo's co-authors include Man-Kwan Shan, Suh-Yin Lee, Hua-Fu Li, Cheng–Te Li, Meng-Che Chuang, Jenq–Neng Hwang and Kresimir Williams and has published in prestigious journals such as Expert Systems with Applications, Multimedia Tools and Applications and IEICE Transactions on Information and Systems.

In The Last Decade

Fang-Fei Kuo

12 papers receiving 243 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fang-Fei Kuo Taiwan	9	141	138	98	57	47	12	271
Nisha Srinivas United States	9	124 0.9×	104 0.8×	78 0.8×	49 0.9×	8 0.2×	12	216
Virach Sornlertlamvanich Thailand	13	93 0.7×	44 0.3×	43 0.4×	390 6.8×	8 0.2×	76	519
Bo Shao China	8	105 0.7×	111 0.8×	34 0.3×	179 3.1×	22 0.5×	21	294
Hermann Hild United States	10	105 0.7×	158 1.1×	19 0.2×	215 3.8×	30 0.6×	16	335
Mark Derthick United States	9	131 0.9×	47 0.3×	32 0.3×	84 1.5×	16 0.3×	23	228
Jaroslav Šeděnka United States	5	106 0.8×	244 1.8×	293 3.0×	82 1.4×	9 0.2×	5	376
Namita Tiwari India	9	103 0.7×	21 0.2×	71 0.7×	98 1.7×	8 0.2×	44	233
Xueqin Chen China	9	150 1.1×	41 0.3×	50 0.5×	192 3.4×	10 0.2×	39	433
Shiai Zhu Canada	10	224 1.6×	36 0.3×	43 0.4×	115 2.0×	11 0.2×	22	336
Donghong Han China	10	47 0.3×	52 0.4×	30 0.3×	181 3.2×	29 0.6×	27	261

Countries citing papers authored by Fang-Fei Kuo

Since Specialization

Citations

This map shows the geographic impact of Fang-Fei Kuo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fang-Fei Kuo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang-Fei Kuo more than expected).

Fields of papers citing papers by Fang-Fei Kuo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang-Fei Kuo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fang-Fei Kuo. The network helps show where Fang-Fei Kuo may publish in the future.

Co-authorship network of co-authors of Fang-Fei Kuo

This figure shows the co-authorship network connecting the top 25 collaborators of Fang-Fei Kuo. A scholar is included among the top collaborators of Fang-Fei Kuo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fang-Fei Kuo. Fang-Fei Kuo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Chuang, Meng-Che, Jenq–Neng Hwang, Fang-Fei Kuo, Man-Kwan Shan, & Kresimir Williams. (2014). Recognizing live fish species by hierarchical partial classification based on the exponential benefit. 5232–5236. 18 indexed citations

2.

Kuo, Fang-Fei, et al.. (2013). MediaEval 2013: Soundtrack Selection for Commercials Based on Content Correlation Modeling. MediaEval. 1 indexed citations

3.

Kuo, Fang-Fei, Man-Kwan Shan, & Suh-Yin Lee. (2013). Background music recommendation for video based on multimodal latent semantic analysis. 1–6. 22 indexed citations

4.

Kuo, Fang-Fei, Cheng–Te Li, Man-Kwan Shan, & Suh-Yin Lee. (2012). Intelligent menu planning. 1–6. 25 indexed citations

5.

Shan, Man-Kwan, et al.. (2008). Relevance feedback for category search in music retrieval based on semantic concept learning. Multimedia Tools and Applications. 39(2). 243–262. 4 indexed citations

6.

Shan, Man-Kwan, et al.. (2008). Emotion-based music recommendation by affinity discovery from film music. Expert Systems with Applications. 36(4). 7666–7674. 48 indexed citations

7.

Li, Hua-Fu, et al.. (2006). A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates. e87 d. 672–676. 15 indexed citations

8.

Li, Hua-Fu, et al.. (2006). Incremental Mining of Sequential Patterns over a Stream Sliding Window. 677–681. 35 indexed citations

9.

Kuo, Fang-Fei, et al.. (2005). Emotion-based music recommendation by association discovery from film music. 507–510. 47 indexed citations

10.

Kuo, Fang-Fei & Man-Kwan Shan. (2004). Looking for new, not known music only. 243–251. 8 indexed citations

11.

Kuo, Fang-Fei & Man-Kwan Shan. (2003). A personalized music filtering system based on melody style classification. 649–652. 20 indexed citations

12.

Shan, Man-Kwan, et al.. (2003). Music style mining and classification by melody. IEICE Transactions on Information and Systems. 86(3). 97–100. 28 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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