Abstract
In this research, we present PerceptHashing, a technique designed to categorize million-scale agricultural scenic images by incorporating human gaze shifting paths (GSPs) into a hashing framework. For each agricultural image, we identify visually and semantically significant object patches, such as fields, crops, and water bodies. These patches are linked to form a graphlet, establishing a network of spatially adjacent patches, and a GSP is then extracted using an active learning algorithm. The GSP reflects the distribution of human gaze across different regions of each agricultural scene, typically involving fewer than 12 regions, as validated by cross-validation. We then design a binary hashing framework that effectively leverages the semantics encoded in these GSPs. This framework integrates three key elements: (i) refinement of noisy labels, (ii) incorporation of deep image-level agricultural semantics, and (iii) updates to the adaptive data graph. The resulting hash codes from each GSP are converted into a kernelized visual descriptor for classification. To evaluate the influence of GSPs on agricultural image classification both qualitatively and quantitatively, we conducted an extensive user study comparing GSPs from typical observers with those from Alzheimer's patients. The results demonstrate that: (1) the classification accuracy of 1.22 million agricultural aerial images using our approach is significantly higher than those processed by other methods, and (2) GSPs from 33 Alzheimer's patients differ markedly from those of 37 normal observers, leading to notable differences in classification accuracy ( accnormal = 0.694 versus accpatient = 0.322 ).