A meta-algorithm for classification by feature nomination

• Published in: Proceedings - International Conference on Image Processing pp. 5187 - 5191
• Main Authors: Sarkar, Rituparna, Skadron, Kevin, Acton, Scott T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2014
• Subjects: Accuracy; classification; Classification algorithms; conditional entropy; Dictionaries; dictionary learning; Entropy; Feature extraction; feature nomination; Image color analysis; Mutual information; sparse representation
• ISSN: 1522-4880
• DOI: 10.1109/ICIP.2014.7026050

With increasing complexity of the dataset it becomes impractical to use a single feature to characterize all constituent images. In this paper we describe a method that will automatically select the appropriate image features that are relevant and efficacious for classification, without requiring modifications to the feature extracting methods or the classification algorithm. We first describe a method for designing class distinctive dictionaries using a dictionary learning technique, which yields class specific sparse codes and a linear classifier parameter. Then, we apply information theoretic measures to obtain the more informative feature relevant to a test image and use only that feature to obtain final classification results. With at least one of the features classifying the query accurately, our algorithm chooses the correct feature in 88.9% of the trials.