D. Guillamet

620 total citations
13 papers, 320 citations indexed

About

D. Guillamet is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing. According to data from OpenAlex, D. Guillamet has authored 13 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 5 papers in Media Technology and 4 papers in Signal Processing. Recurrent topics in D. Guillamet's work include Image Retrieval and Classification Techniques (9 papers), Face and Expression Recognition (7 papers) and Advanced Image and Video Retrieval Techniques (6 papers). D. Guillamet is often cited by papers focused on Image Retrieval and Classification Techniques (9 papers), Face and Expression Recognition (7 papers) and Advanced Image and Video Retrieval Techniques (6 papers). D. Guillamet collaborates with scholars based in Spain, Switzerland and Japan. D. Guillamet's co-authors include Jordi Vitrià, Bernt Schiele, Marco Bressan and B. Moghaddam and has published in prestigious journals such as Pattern Recognition, Pattern Recognition Letters and Cybernetics & Systems.

In The Last Decade

D. Guillamet

13 papers receiving 294 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
D. Guillamet Spain	9	175	104	72	51	50	13	320
Zhen Lou China	7	304 1.7×	75 0.7×	70 1.0×	80 1.6×	33 0.7×	21	408
Songcan Chen China	9	215 1.2×	42 0.4×	138 1.9×	54 1.1×	27 0.5×	15	305
Shingo Tomita Japan	8	194 1.1×	64 0.6×	125 1.7×	57 1.1×	11 0.2×	20	318
Zhaoshui He China	4	102 0.6×	152 1.5×	84 1.2×	15 0.3×	77 1.5×	6	334
Omar Nibouche United Kingdom	13	172 1.0×	96 0.9×	69 1.0×	59 1.2×	6 0.1×	44	456
Eiji Kawaguchi Japan	13	684 3.9×	75 0.7×	130 1.8×	80 1.6×	20 0.4×	63	768
Chee-Way Chong Malaysia	7	504 2.9×	30 0.3×	38 0.5×	97 1.9×	15 0.3×	11	585
Yuanyuan Jiao China	8	232 1.3×	24 0.2×	144 2.0×	73 1.4×	68 1.4×	22	343
Ching-Min Cheng Taiwan	7	203 1.2×	39 0.4×	19 0.3×	49 1.0×	29 0.6×	18	293
Gui-Fu Lu China	12	341 1.9×	46 0.4×	127 1.8×	84 1.6×	57 1.1×	35	412

Countries citing papers authored by D. Guillamet

Since Specialization

Citations

This map shows the geographic impact of D. Guillamet'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 D. Guillamet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Guillamet more than expected).

Fields of papers citing papers by D. Guillamet

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. Guillamet. 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 D. Guillamet. The network helps show where D. Guillamet may publish in the future.

Co-authorship network of co-authors of D. Guillamet

This figure shows the co-authorship network connecting the top 25 collaborators of D. Guillamet. A scholar is included among the top collaborators of D. Guillamet 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 D. Guillamet. D. Guillamet 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:

13 of 13 papers shown

1.

Guillamet, D., Marco Bressan, & Jordi Vitrià. (2005). A weighted non-negative matrix factorization for local representations. 1. I–942. 33 indexed citations

2.

Bressan, Marco, D. Guillamet, & Jordi Vitrià. (2005). Using an ICA representation of high dimensional data for object recognition and classification. 1. I–1004. 11 indexed citations

3.

Bressan, Marco, D. Guillamet, & Jordi Vitrià. (2004). MULTICLASS OBJECT RECOGNITION USING CLASS-CONDITIONAL INDEPENDENT COMPONENT ANALYSIS. Cybernetics & Systems. 35(1). 35–61. 1 indexed citations

4.

Guillamet, D. & Jordi Vitrià. (2003). Determining a suitable metric when using non-negative matrix factorization. 2. 128–131. 15 indexed citations

5.

Guillamet, D., Jordi Vitrià, & Bernt Schiele. (2003). Introducing a weighted non-negative matrix factorization for image classification. Pattern Recognition Letters. 24(14). 2447–2454. 120 indexed citations

6.

Bressan, Marco, D. Guillamet, & Jordi Vitrià. (2003). Using an ICA representation of local color histograms for object recognition. Pattern Recognition. 36(3). 691–701. 19 indexed citations

7.

Guillamet, D., Bernt Schiele, & Jordi Vitrià. (2003). Analyzing non-negative matrix factorization for image classification. 2. 116–119. 52 indexed citations

8.

Moghaddam, B., D. Guillamet, & Jordi Vitrià. (2003). Local appearance-based models using high-order statistics of image features. I–729. 4 indexed citations

9.

Guillamet, D. & Jordi Vitrià. (2003). Evaluation of distance metrics for recognition based on non-negative matrix factorization. Pattern Recognition Letters. 24(9-10). 1599–1605. 24 indexed citations

10.

Guillamet, D. & Jordi Vitrià. (2002). Classifying Faces with Non-negative Matrix Factorization. 21 indexed citations

11.

Guillamet, D. & Jordi Vitrià. (2002). A comparison of global versus local color histograms for object recognition. 2. 422–425. 10 indexed citations

12.

Guillamet, D. & Jordi Vitrià. (2002). Discriminant basis for object classification. 1. 256–261. 5 indexed citations

13.

Guillamet, D. & Bernt Schiele. (2001). Color Histogram Classification using NMF. 5 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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