Çiçek Türlerinin Tanınması için ESA Tabanlı Transfer Öğrenme Üzerine Bir Çalışma

Öz

Bitki organlarından biri olan çiçek, ekolojik düzenin önemli bir elementidir. Çiçekler insanlara faydalı olan birçok alanda kullanılmıştır. Günümüzde bilinen yaklaşık dört yüz bin çiçek çeşidi vardır. Çiçekleri şekil ve renk benzerliği nedeniyle birbirinden ayırt etmek zor bir iştir. Çiçek sınıflandırması, çok çeşitli şekiller, renk dağılımı, aydınlatma koşulları ve maruz kalma deformasyonu nedeniyle zorlu bir problemdir. Renk ve şekil olarak birbirine benzeyen çiçekleri insan gözüyle ayırt etmek bazı görüntülerde daha da zorlaşmaktadır. İnsanların belirli türleri doğru bir şekilde ayırt etmesi dikkate değer bir eğitim gerektirir ve genellikle çok spesifik morfolojik özellikler, yakından ilişkili türleri ayırt eden tek şeydir. ESA modelleri son zamanlarda araştırmacılar tarafından manuel özniteliklere olan ihtiyacı ortadan kaldırmak için birçok sınıflandırma probleminde kullanılmaktadır. Bu çalışmada, çiçek türlerinin tanınması için ESA tabanlı transfer öğrenme yöntemleri incelenmiştir. Çiçek türlerinin sınıflandırılması için, önceden eğitilmiş popüler öğrenme tekniklerden VGG16, VGG19, SqueezeNet, DenseNet-121, DenseNet-201 ve InceptionResNetV2 uygulanmaktadır. Deneysel sonuçlarda aynı çiçek veri kümesi üzerinde sınıflandırma performansları karşılaştırılmıştır. Bu çalışmada InceptionResNetV2 modelinin diğer modellere göre daha üstün sonuçlar verdiği gözlemlenmiştir. En yüksek doğruluk (%92.25), çiçek veri seti için InceptionResNetV2 modeliyle elde edilmiştir.

Anahtar Kelimeler

• Derin öğrenme
• Transfer öğrenme
• Evrişimli sinir ağları
• Öznitelik seçme
• Çiçek türleri.

A Study on CNN Based Transfer Learning for Recognition of Flower Species

Abstract

The flower that is one of the plant organs, is essential element of the ecological order. Flowers have been used in many areas that are beneficial to humans. There exist about four hundred thousand varieties of flowers known today. It is a difficult task to distinguish flowers from each other due to their similarity in shape and color. Flower classification is a challenging problem due to the high variety of shapes, color distribution, lighting conditions and deformation of exposure. It becomes more difficult to distinguish flowers that are similar in color and shape to each other with the human eye for some images. It takes remarkable training for humans to correctly distinguish between particular species, and often very specific morphological features are the only thing that distinguishes closely related species. CNN models have been recently used by researchers in many classification problems to eliminate the need for manual features. In this study, CNN-based transfer learning methods are studied for recognition of flower species. Popular pretrained learning techniques which are VGG16, VGG19, SqueezeNet, DenseNet-121, DenseNet-201, and InceptionResNetV2 are conducted for classification of flower species. Their classification performances are compared on same flower dataset in experimental results. It was observed that the InceptionResNetV2 model gives superior results than other models in experiments. The highest accuracy (92.25%) is obtained with the InceptionResNetV2 model for flower dataset.

Keywords

• Deep learning
• Transfer learning
• Convolutional neural network
• Feature selection
• Flower species

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