Parallel Deep Learning with Suggestive Activation for Object Category Recognition

Abstract

The performance of visual perception algorithms for object category detection has largely been restricted by the lack of generalizability and scalability of state-of-art hand-crafted feature detectors and descriptors across instances of objects with different shapes, textures etc. The recently introduced deep learning algorithms have attempted at overcoming this limitation through automatic learning of feature kernels. Nevertheless, conventional deep learning architectures are uni-modal, essentially feedforward testing pipelines working on image space with little regard for context and semantics. In this paper, we address this issue by presenting a new framework for object categorization based on Deep Learning, called Parallel Deep Learning with Suggestive Activation (PDLSA) that imbibes several brain operating principles drawn from neuroscience and psychophysical studies. In particular, we focus on Suggestive Activation – a schema which enables feedback loops in the recognition process that use information obtained from partial detection results to generate hypotheses based on long-term memory (or knowledge base) to search in the image space for features corresponding to these hypotheses thereby enabling activation of the response corresponding to the correct object category through multi-modal integration. Results presented against a traditional SIFT based category classifier on the University of Washington benchmark RGB-D dataset demonstrates the validity of the approach.