{'fraction_non_alphanumeric': 0.013904338153503892, 'fraction_numerical': 0.0, 'mean_word_length': 5.518115942028985, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Important differences between distance classifier correlation filters and Fisher linear discriminant functions

Recent advances in the design of correlation filters have made them an important tool for automatic target recognition (ATR). In particular, Distance Classifier Correlation filters (DCCFs) introduced by Mahalanobis et al. have proven attractive in ATR experiments. This success of DCCFs is mainly due to a methodology that is designed to provide good distortion tolerance and discrimination. Since DCCFs maximize 'distances' between classes, there is a natural tendency to misinterpret DCCFs as nothing more than another form of Fisher Linear Discriminant Functions (LDFs). The DCCF algorithm combines properties of a correlator with signal processing techniques and concepts to yield novel and efficient ways for recognizing targets in images. Although parallels exist between the DCCF algorithm and some well known concepts in pattern recognition such as the Fisher LDF, several important differences between the two should be clearly understood. Fundamentally, the Fisher LDF is a vector onto which feature vectors are projected whereas the DCCFs require quadratic distance calculations in a transformed space. Fisher LDFs are usually applied to features computed from an image whereas DCCFs are designed to work with images. These and other important differences between the two approaches lead to very different end results. We will clearly identify these differences and also point out the apparent similarities that lead to potential confusion between the two algorithms. A subset of the public release MSTAR data will be used for illustrative comparisons. This clarifying presentation is essential for a better understanding of DCCFs and a fair comparison with other approaches.