UC Riverside

© 2015 Elsevier Ltd. Manually collecting, identifying, archiving and retrieving specimen images is an expensive and time-consuming work for entomologists. There is a clear need to introduce fast systems integrated with modern image processing and analysis algorithms to accelerate the process. In this paper, we describe the development of an automated moth species identification and retrieval system (SPIR) using computer vision and pattern recognition techniques. The core of the system is a probabilistic model that infers Semantically Related Visual (SRV) attributes from low-level visual features of moth images in the training set, where moth wings are segmented into information-rich patches from which the local features are extracted, and the SRV attributes are 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 is evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further propose 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 constructs a network with nodes representing the SRV attributes and weighted edges denoting the co-occurrence correlation. A fast modularity maximization algorithm is proposed to detect the co-occurrence patterns as communities in the network. A random walk process working on the discovered co-occurrence patterns is applied to refine the individual attribute detection results. The effectiveness of the proposed approach is evaluated in automated moth identification and attribute-based image retrieval. In addition, a novel image descriptor called SRV attribute signature is introduced to record the visual and semantic properties of an image and is used to compare image similarity. Experiments are performed on an existing entomology database to illustrate the capabilities of our proposed system. We observed that the system performance is improved by the SRV attribute representation and their co-occurrence patterns.