How Can Human Hierarchical and Active Visual Perceptual Behavior Influence Scenic Image Categorization? A Deep Vision Model for Architectural Scenery Toward Education

Exploring the complex meanings in architectural scenic images is crucial for modern intelligent systems. We propose a novel method combining multi-channel perceptual features to semantically understand scenes with intricate spatial structures. Our deep hierarchical model captures human gaze dynamics using a binary objectness measure to detect objects and details at various architectural scales. We introduce a multi-layer active learning paradigm to derive gaze shifting paths (GSPs) and calculate corresponding deep representations, tolerant to label noise via a penalty term that discards irrelevant GSP features. A manifold-regularized feature selector is used to choose high-quality GSP features, and a linear classifier distinguishes architectural scenes. Our technique outperforms traditional models, as shown in tests on three image retrieval datasets, demonstrating the discriminative capacity of our deep gaze-guided features.