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Improved Feature Generalization in Smaller Datasets with Early Feature Fusion of Handcrafted and Automated Features for Content Based Image Classification

Diverse techniques of feature extraction are carefully crafted by researchers in the domain of content based image classification for improved categorization of image data. These are handcrafted techniques of descriptor definition which have mostly emphasized in representing a particular feature of the query image. Recent advancements in deep neural networks have enabled extraction of features in automated manner without any human intervention. The process considers multiple dimensions of image properties in designing content based descriptors. However, deep features have shown limitations for generalization of feature vectors when number of training instances are small. The pre-trained CNN networks have also revealed limitations in generalization of extracted features from smaller datasets. This work has attempted to address this issue by designing fusion based architecture. In the process, the authors have experimented with features extracted with individual techniques comprising of handcrafted extraction and automated neural network based extraction from a small dataset. The process of feature extraction is carried out on an imbalanced dataset for which the classification evaluation is done with F1 Score. The experimental process has revealed better feature generalization with enhanced classification performances by application of early fusion of extracted features compared to that of features extracted with single techniques.