Transform-invariant Image Decomposition with Similarity Templates

Recent work has shown impressive transform-invariant modeling and clustering for sets of images of objects with similar appearance. We seek to expand these capabilities to sets of images of an object class that show considerable variation across individual instances (e.g. pedestrian images) using a representation based on pixel-wise similarities, similarity templates. Because of its invariance to the colors of particular components of an object, this representation enables detection of instances of an object class and enables alignment of those instances. Further, this model implicitly represents the regions of color regularity in the class-specific image set enabling a decomposition of that object class into component regions.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Chris Stauffer, Erik G. Miller, Kinh Tieu
• Keywords: Impressive Transform-invariant Modeling, NIPS 2001, NIPS 2007, Object Class, Representation Enables Detection