Novel Object Discovery Using Case-Based Reasoning and Convolutional Neural Networks

Abstract

The development of Convolutional Neural Networks (CNNs) has resulted in significant improvements to object classification and detection in image data. One of their primary benefits is that they learn image features rather than relying on hand-crafted features, thereby reducing the amount of knowledge engineering that must be performed. However, another form of knowledge engineering bias exists in how objects are labelled in images, thereby limiting CNNs to classifying the set of object types that have been predefined by a domain expert. We describe a case-based method for detecting novel object types using a combination of an image’s raw pixel values and detectable parts. Our approach works alongside existing CNN architectures, thereby leveraging the state-of-the-art performance of CNNs, and is able to detect novel classes using limited training instances. We evaluate our approach using an existing object detection dataset and provide evidence of our approach’s ability to classify images even if the object in the image has not been previously encountered.