Classification of Blurred Flowers Using Convolutional Neural Networks

In recent years, flower image processing has been improved dramatically with the help of deep learning, notably convolutional neural network related algorithms. With the advent of smart devices, it becomes much easier to take photos of wild flowers for non-professional people. However, this raises new challenges in flower image processing due to blur effect. In this paper, we present a two-step automatic classification method based on a convolutional neural network for wild flowers. We first preprocess flower images by classifying them as either blurred or clear images. Then we extract features of clear images using the convolutional neural network and classify them accordingly with these features. Results show that we can improve the classification rate by 33.78%, reaching an accuracy of 90.20%.

[1]  David Zhang,et al.  Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification , 2014, International Journal of Computer Vision.

[2]  Qi Tian,et al.  Image Classification and Retrieval are ONE , 2015, ICMR.

[3]  Yong Wu,et al.  Convolution Neural Network based Transfer Learning for Classification of Flowers , 2018, 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP).

[4]  Heba Saadeh,et al.  Flower classification using deep convolutional neural networks , 2018, IET Comput. Vis..

[5]  Jie Zou,et al.  Evaluation of model-based interactive flower recognition , 2004, ICPR 2004.

[6]  Qi Tian,et al.  Hierarchical deep semantic representation for visual categorization , 2017, Neurocomputing.

[7]  Qi Tian,et al.  Towards Reversal-Invariant Image Representation , 2017, International Journal of Computer Vision.

[8]  Xiu-Shen Wei,et al.  Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval , 2016, IEEE Transactions on Image Processing.

[9]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[10]  Shuicheng Yan,et al.  LG-CNN: From local parts to global discrimination for fine-grained recognition , 2017, Pattern Recognit..

[11]  Tzu-Hsiang Hsu,et al.  An interactive flower image recognition system , 2010, Multimedia Tools and Applications.

[12]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Xiaoling Xia,et al.  Inception-v3 for flower classification , 2017, 2017 2nd International Conference on Image, Vision and Computing (ICIVC).

[14]  Shuicheng Yan,et al.  Task-Driven Feature Pooling for Image Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[15]  Fahad Shahbaz Khan,et al.  Modulating Shape Features by Color Attention for Object Recognition , 2012, International Journal of Computer Vision.

[16]  M. Thilagavathi,et al.  Cascade-Forward Neural Network in Identification of Plant Species of Desert Based on Wild Flowers , 2018, 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA).

[17]  Rong Jin,et al.  Fine-grained visual categorization via multi-stage metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Yu Liu,et al.  On the Exploration of Convolutional Fusion Networks for Visual Recognition , 2016, MMM.

[19]  Rogério Schmidt Feris,et al.  Fusing well-crafted feature descriptors for efficient fine-grained classification , 2014, ICIP.

[20]  Umapada Pal,et al.  Collaborative representation based fine-grained species recognition , 2016, 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ).

[21]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Xiaogang Jin,et al.  Two-level hierarchical feature learning for image classification , 2016, Frontiers of Information Technology & Electronic Engineering.

[23]  Weiming Dong,et al.  Flower classification via convolutional neural network , 2016, 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA).