| Publications of year 2005 |
| Articles in journal or book's chapters |
@InBook{MRJ05,
author = {Manouvrier, M. and Rukoz, M. and Jomier, G.},
title = {Spatial Databases: Technologies, Techniques and Trend},
chapter = {IV - Quadtree-Based Image Representation and Retrieval},
publisher = {Y. Manolopoulos, A. Papadopoulos and M. Vassilakopoulos (Eds), IDEA Group Publishing, Information Science Publishing and IRM Press},
year = {2005},
url = {http://delab.csd.auth.gr/sdttt/},
abstract = {This chapter is a survey of quadtree uses in the image domain from image representation, to image storage and content-based retrieval. A quadtree is a spatial data structure built by a recursive decomposition of space into quadrants. Applied to images, it allows representing image content, compacting or compressing image information, and querying images. For thirteen years, numerous image-based approaches have used this structure. In this chapter, the authors want to underline the contribution of quadtree in image applications.},
pages = {81--106}
}
| Conference's articles |
@InProceedings{JMO+05,
author = {Jomier, G. and Manouvrier, M. and Oria, V. and Rukoz, M.},
title = {Multilevel Index for Global and Partial Content-Based Image Retrieval},
booktitle = {Proc. of the 1st IEEE Int. Workshop on Managing Data for Emerging Multimedia Applications (EMMA'05)},
year = {2005},
address = {Tokyo (Japan)},
pages = {66--75},
month = {April 8-9th},
abstract = {This article presents a quadtree-based data structure for effective indexing of images. An image is represented by a multi-level feature vector, computed by a recursive decomposition of the image into four quadrants and stored as a full fixed-depth balanced quadtree. A node of the quadtree stores a feature vector of the corresponding image quadrant. A more general quadtree-based structure called QUIP-tree (QUadtree-based Index for image retrieval and Pattern search ) is used to index the multi-level feature vectors of the images and their quadrants. A QUIP-tree node is an entry to a set of clusters that groups similar quadrants according to some pre-defined distances. The QUIP-tree allows a multi-level filtering in content-based image retrieval as well as partial queries on images.},
note = {in conjunction with 21th IEEE Conference on Data Engineering (ICDE)}
}
@InProceedings{MRJ05b,
author = {Manouvrier, M. and Rukoz, M. and Jomier, G.},
title = {A generalized metric distance between hierarchically partitioned images},
booktitle = {Proc. of the Sixth Intl. Workshop on Multimedia Data Mining - "Mining Integrated Media and Complex Data" (MDM/KDD2005)},
year = {2005},
address = {Chicago (USA)},
month = {Aug.},
page = {33--41},
note = {In conjunction with the Eleventh ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining},
pdf = {http://www.lamsade.dauphine.fr/bdgl/PS/MMMRGJ_MDM_KDD2005.pdf},
abstract = {This article presents a generalized metric distance, called $\Delta$-distance, between images represented by a tree structure resulting from a recursive image partition. This distance is used to perform content-based image retrieval queries in databases. $\Delta$-distance allows to retrieve images globally similar to a query image. This distance takes into account the location of the image visual features. It can be performed using a multi-level filtering algorithm. Moreover, $\Delta$-distance allows region-based queries. In this case, the resulting images contain quadrants similar to the quadrants selected by the user in the query image or contain quadrants similar to the entire query image. Because it is a generalized distance function, some particular cases of the $\Delta$-distance appear in existing content-based image retrieval systems.},
url = {http://www.lamsade.dauphine.fr/~manouvri/Slides_MDM_KDD2005.pdf},
}