Building a Classification Cascade for Visual Identification from One Example

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Inter-class variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle objectspecific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik
• Keywords: Candidate Feature, Computer Vision, Discriminative Feature, Feature Sequence Unique, ICCV 2005, Off-line Training Procedure, Specific Model Image