The UvA-LINKER will give you a range of other options to find the full text of a publication (including a direct link to the full-text if it is located on another database on the internet).
De UvA-LINKER biedt mogelijkheden om een publicatie elders te vinden (inclusief een directe link naar de publicatie online als deze beschikbaar is in een database op het internet).

Search results

Query: faculty: "FNWI" and publication year: "2010"

AuthorsK.E.A. van de Sande, T. Gevers, C.G.M. Snoek
TitleEvaluating Color Descriptors for Object and Scene Recognition
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume32
Year2010
Issue9
Pages1582-1596
ISSN01628828
FacultyFaculty of Science
Institute/dept.FNWI: Informatics Institute (II)
AbstractImage 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.
Document typeArticle
Document finderUvA-Linker