NDLI: Automated identification and retrieval of moth images with semantically related visual attributes on the wings

Content Provider	IEEE Xplore Digital Library
Author	Linan Feng Bhanu, B.
Copyright Year	2013
Description	Author affiliation: Center for Res. in Intell. Syst., Univ. of California, Riverside, Riverside, CA, USA (Linan Feng; Bhanu, B.)
Abstract	A new automated identification and retrieval system is proposed that aims to provide entomologists, who manage insect specimen images, with fast computer-based processing and analyzing techniques. Several relevant image attributes were designed, such as the so-called semantically-related visual (SRV) attributes detected from the insect wings and the co-occurrence patterns of the SRV attributes which are uncovered from manually labeled training samples. A joint probabilistic model is used as SRV attribute detector working on image visual contents. The identification and retrieval of moth species are conducted by comparing the similarity of SRV attributes and their co-occurrence patterns. The prototype system used moth images while it can be generalized to any insect species with wing structures. The system performed with good stability and the accuracy reached 85% for species identification and 71% for content-based image retrieval on a entomology database.
Starting Page	2577
Ending Page	2581
File Size	532069
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738531
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	attribute cooccurrence pattern detection Entomological image identification and retrieval semantically-related visual attribtues
Content Type	Text
Resource Type	Article

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