Context-Based Object-Class Recognition and Retrieval by Generalized
Correlograms
Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29 (10):
1818-1833 (October 2007)DOI: 10.1109/TPAMI.2007.1098
Abstract
We present a novel approach for retrieval of object categories based
on a novel type of image representation: the generalized correlogram
(GC). In our image representation, the object is described as a constellation
of GCs, where each one encodes information about some local part
and the spatial relations from this part to others (that is, the
part's context). We show how such a representation can be used with
fast procedures that learn the object category with weak supervision
and efficiently match the model of the object against large collections
of images. In the learning stage, we show that, by integrating our
representation with Boosting, the system is able to obtain a compact
model that is represented by very few features, where each feature
conveys key properties about the object's parts and their spatial
arrangement. In the matching step, we propose direct procedures that
exploit our representation for efficiently considering spatial coherence
between the matching of local parts. Combined with an appropriate
data organization such as inverted files, we show that thousands
of images can be evaluated efficiently. The framework has been applied
to different standard databases, and we show that our results are
favorably compared against state-of-the-art methods in both computational
cost and accuracy.