We propose a model for classification and detection of object classes where the number of classes may be large and where multiple instances of object classes may be present in an image. The algorithm combines a bottom-up, low-level, procedure of a bag-of-words naive Bayes phase for winnowing out unlikely object classes with a high-level procedure for detection and classification. The high-level process is a hybrid of a voting method where votes are filtered using beliefs computed by a class-specific graphical model. In that sense, shape is both explicit (determining the voting pattern) and implicit (each object part votes independently) - hence the term "semi-explicit shape model".
|Original language||American English|
|Title of host publication||Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings|
|Number of pages||14|
|ISBN (Print)||3642155510, 9783642155512|
|State||Published - 2010|
|Event||11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece|
Duration: 10 Sep 2010 → 11 Sep 2010
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||11th European Conference on Computer Vision, ECCV 2010|
|Period||10/09/10 → 11/09/10|
Bibliographical noteFunding Information:
This work was partially funded by ISF grant 519/09.