Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached $CDATA[$CDATA[$>$$92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and $CDATA[$CDATA[$>$$96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.
... - (that is, Sub-) Random Sequences 403 -- 7.9 Adaptive and Recursive Monte Carlo Methods 410 -- 8 Sorting ... ... 16.3 Markov Models and Hidden Markov Modeling 856 -- 16.4 Hierarchical Clustering by Phylogenetic Trees ... ... Point 962 -- 18.3 Relaxation Methods 964 -- 18.4 A Worked Example: Spheroidal Harmonics 971 -- 18.5 Automated ... ... 21 Computational Geometry 1097 -- 21.0 Introduction 1097 -- 21.1 Points and Boxes 1099 -- 21.2 KD Trees ...
3rd ed. xxi, 1235 s. : il. ; 27 cm + 1 CD-ROM
- MeSH
- Mathematical Computing MeSH
- Mathematics MeSH
- Numerical Analysis, Computer-Assisted * MeSH
- Publication type
- Monograph MeSH
- Conspectus
- Počítačová věda. Výpočetní technika. Informační technologie
- NML Fields
- přírodní vědy
- přírodní vědy
... Precisely -- Determined by Mass Spectrometry 89 -- 4.2 Amino Acid Sequences Can Be Determined by Automated ... ... Possible the -- Visualization of Proteins in the Cell 103 -- 4.4 Peptides Can Be Synthesized by Automated ... ... of Replication (Sanger Dideoxy Method) 146 -- 6.1.4 DNA Probes and Genes Can Be Synthesized by -- Automated ... ... Comparison of RNA Sequences Can Be a Source of Insight into Secondary Structures 182 -- 7.4 Evolutionary Trees ... ... Carry Proteins from the Endoplasmic Reticulum to the Golgi Complex for -- Further Glycosylation and Sorting ...
5th ed. xvii, 974 s. : il., tab., grafy ; 32 cm
