Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition
This work addresses multi-class object recognition in computer vision, presenting an incremental improvement by generalizing existing hierarchical methods into a graph-based framework.
The authors tackled the problem of multi-class visual recognition by proposing a Classifier Graph model that integrates hierarchical approaches, enabling rich interactions between classes at different abstraction levels, and achieved efficient learning through step-by-step updates with a recursive structure.
We propose a general multi-class visual recognition model, termed the Classifier Graph, which aims to generalize and integrate ideas from many of today's successful hierarchical recognition approaches. Our graph-based model has the advantage of enabling rich interactions between classes from different levels of interpretation and abstraction. The proposed multi-class system is efficiently learned using step by step updates. The structure consists of simple logistic linear layers with inputs from features that are automatically selected from a large pool. Each newly learned classifier becomes a potential new feature. Thus, our feature pool can consist both of initial manually designed features as well as learned classifiers from previous steps (graph nodes), each copied many times at different scales and locations. In this manner we can learn and grow both a deep, complex graph of classifiers and a rich pool of features at different levels of abstraction and interpretation. Our proposed graph of classifiers becomes a multi-class system with a recursive structure, suitable for deep detection and recognition of several classes simultaneously.