Learning Image to Symbol Conversion

Abstract

A common paradigm in object recognition is to extract symbolic and/or numeric features from an image as a preprocessing step for classification. The machine learning and pattern recognition communities have produced many techniques for classifying instances given such features. In contrast, learning to extract a distinguishing set of features that will lead to unambiguous instance classification has received comparatively little attention. We propose a learning paradigm that integrates feature extraction and classifier induction, exploiting their close interrelationship to give improved classification performance. Introduction Object recognition systems can conceptually be divided into two phases: feature extraction and recognition. In the feature extraction phase, feature vectors are extracted for each instance. In general, the features are hand selected, as are their parameters, for example the cut-off frequencies of a bandpass filter or the window size of a convolution operator. In..