kbibtex-testset/bib/mendeley2.bib

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0003
0004 @article{Rosten2006a,
0005 author = {Rosten, Edward and Drummond, Tom},
0006 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/10.1.1.60.3991.pdf:pdf},
0007 journal = {ECCV 2006},
0008 pages = {430--443},
0009 publisher = {Springer},
0010 title = {{Machine learning for high-speed corner detection}},
0011 year = {2006}
0012 }
0013 @article{Carletta2007,
0014 author = {Carletta, Jean},
0015 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/Carletta - 2007 - Unleashing the killer corpus experiences in creating the multi-everything AMI Meeting Corpus.pdf:pdf},
0016 journal = {Language Resources and Evaluation},
0017 keywords = {annotated corpora,discourse annotation,meetings},
0018 number = {2},
0019 pages = {181--190},
0020 publisher = {Springer},
0021 title = {{Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus}},
0022 url = {http://www.springerlink.com/index/N8801047264109J1.pdf},
0023 volume = {41},
0024 year = {2007}
0025 }
0026 @article{VandeSande2010,
0027 abstract = {Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.},
0028 author = {van de Sande, Koen E a and Gevers, Theo and Snoek, Cees G M},
0029 doi = {10.1109/TPAMI.2009.154},
0030 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/GeversPAMI10.pdf:pdf},
0031 issn = {1939-3539},
0032 journal = {IEEE transactions on PAMI},
0033 keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Color,Colorimetry,Colorimetry: methods,Computer-Assisted,Computer-Assisted: methods,Image Enhancement,Image Enhancement: methods,Image Interpretation,Imaging,Pattern Recognition,Reproducibility of Results,Sensitivity and Specificity,Three-Dimensional,Three-Dimensional: methods},
0034 month = sep,
0035 number = {9},
0036 pages = {1582--96},
0037 pmid = {20634554},
0038 title = {{Evaluating color descriptors for object and scene recognition.}},
0039 url = {http://www.ncbi.nlm.nih.gov/pubmed/20634554},
0040 volume = {32},
0041 year = {2010}
0042 }
0043 @article{Zivkovic2006a,
0044 author = {Zivkovic, Zoran and van der Heijden, Ferdinand},
0045 doi = {10.1016/j.patrec.2005.11.005},
0046 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/zivkovicPRL2006.pdf:pdf},
0047 issn = {01678655},
0048 journal = {Pattern Recognition Letters},
0049 keywords = {background subtraction,gaussian mixture model,non-parametric density estimation,on-line density estimation},
0050 month = may,
0051 number = {7},
0052 pages = {773--780},
0053 title = {{Efficient adaptive density estimation per image pixel for the task of background subtraction}},
0054 volume = {27},
0055 year = {2006}
0056 }
0057 @inproceedings{KaewTraKulPong2001,
0058 author = {KaewTraKulPong, P. and Bowden, R},
0059 booktitle = {Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems},
0060 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/KaewTraKulPong-AVBS01.pdf:pdf},
0061 pages = {1--5},
0062 title = {{An improved adaptive background mixture model for real-time tracking with shadow detection}},
0063 volume = {25},
0064 year = {2001}
0065 }
0066 @article{Calonder2010a,
0067 author = {Calonder, Michael and Lepetit, Vincent and Strecha, C. and Fua, Pascal},
0068 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/10.1.1.175.2122.pdf:pdf},
0069 journal = {ECCV 2010},
0070 pages = {778--792},
0071 publisher = {Springer},
0072 title = {{Brief: Binary robust independent elementary features}},
0073 year = {2010}
0074 }
0075 @article{Everingham2009,
0076 author = {Everingham, Mark and Gool, Luc and Williams, Christopher K. I. and Winn, John and Zisserman, Andrew},
0077 doi = {10.1007/s11263-009-0275-4},
0078 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/everingham10.pdf:pdf},
0079 issn = {0920-5691},
0080 journal = {International Journal of Computer Vision},
0081 keywords = {benchmark,database,object recognition},
0082 month = sep,
0083 number = {2},
0084 pages = {303--338},
0085 title = {{The Pascal Visual Object Classes (VOC) Challenge}},
0086 volume = {88},
0087 year = {2009}
0088 }
0089 @article{Bernardin2008a,
0090 author = {Bernardin, Keni and Stiefelhagen, Rainer},
0091 doi = {10.1155/2008/246309},
0092 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/246309.pdf:pdf},
0093 issn = {1687-5176},
0094 journal = {EURASIP Journal on Image and Video Processing},
0095 pages = {1--10},
0096 title = {{Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics}},
0097 volume = {2008},
0098 year = {2008}
0099 }
0100 @article{Vondrick2010,
0101 author = {Vondrick, Carl and Ramanan, Deva and Patterson, Donald},
0102 file = {:C$\backslash$:/Users/BarisEvrim/Documents/Mendeley Desktop/scalingup.pdf:pdf},
0103 journal = {ECCV 2010},
0104 pages = {610--623},
0105 publisher = {Springer},
0106 title = {{Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces}},
0107 year = {2010}
0108 }