I-FAC: Efficient Fuzzy Associative Classifier for Object Classes in Images

Abstract

We present I-FAC, a novel fuzzy associative classification algorithm for object class detection in images using interest points. In object class detection, the negative class C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> is generally vague (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> = U - C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> ; where U and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sub> are the universal and positive classes respectively). But, image classification necessarily requires both positive and negative classes for training. I-FAC is a single class image classifier that relies only on the positive class for training. Because of its fuzzy nature, I-FAC also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged from its false-positive-rate(FPR)-versus-recall curve is very good, especially at lower FPRs when its recall is even better. IFAC has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily, unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on standard public datasets), the performance of I-FAC is much better, especially at lower FPRs, than that of either bag-of-words (BOW) or SVM (both using interest points).