Described is the development of an automated moth species identification and retrieval system (SPIR) using computer vision and pattern recognition techniques. The core of the system was a probabilistic model that infered Semantically Related Visual (SRV) attributes from low-level visual features of moth images in the training set, where moth wings were segmented into information-rich patches from which the local features were extracted, and the SRV attributes were provided by human experts as ground-truth. For the large amount of unlabeled test images in the database or added into the database later on, an automated identification process was evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further proposed a novel network analysis based approach to explore and utilize the co-occurrence patterns of SRV attributes as contextual cues to improve individual attribute detection accuracy. Working with a small set of labeled training images, the approach constructed a network with nodes representing the SRV attributes and weighted edges denoting the co-occurrence correlation.