Flower image classification with basket of features and multi layered artificial neural networks

Artificial intelligence is penetrating most of the classification and recognition tasks performed by a computer. This work proposes to classify flower images based on features extracted during segmentation and after segmentation using multiple layered neural networks. The segmentation models used are watershed, wavelet, wavelet fusion model, supervised active contours based on shape, color and Local binary pattern textures and color, fused textures based active contours. Multi-dimension feature vectors are constructed from these segmented results for each indexed flower image labelled with their name. Each feature becomes input to a neuron in various feature layers and error back propagation algorithm with convex optimization structure trains these multiple feature layers. Testing with different flower images sets from multiple sources resulted in average classification accuracy of 92% for shape, color and texture supervised active contour segmented flower images.

[1]  Syed Inthiyaz,et al.  Flower segmentation with level sets evolution controlled by colour, texture and shape features , 2017 .

[2]  Manish Khare,et al.  Object tracking using combination of daubechies complex wavelet transform and Zernike moment , 2015, Multimedia Tools and Applications.

[3]  Gang Chen,et al.  A flower image retrieval method based on ROI feature , 2004, Journal of Zhejiang University. Science.

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

[5]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[6]  Francesco Bianconi,et al.  Evaluation of the effects of Gabor filter parameters on texture classification , 2007, Pattern Recognit..

[7]  E. Zagrouba,et al.  Flower image segmentation based on color analysis and a supervised evaluation , 2012, 2012 International Conference on Communications and Information Technology (ICCIT).

[8]  P. V. V. Kishore,et al.  Visual-verbal machine interpreter for sign language recognition under versatile video backgrounds , 2014, 2014 First International Conference on Networks & Soft Computing (ICNSC2014).

[9]  K. V. V. Kumar,et al.  Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion , 2017 .

[10]  Mohan S. Kankanhalli,et al.  Perspectives on Content-Based Multimedia Systems , 2000, The Information Retrieval Series.

[11]  Paul F. Whelan,et al.  Image segmentation based on the integration of colour-texture descriptors - A review , 2011, Pattern Recognit..

[12]  Fadzilah Siraj,et al.  Digital Image Classification for Malaysian Blooming Flower , 2010, 2010 Second International Conference on Computational Intelligence, Modelling and Simulation.

[13]  Jie Zou,et al.  Evaluation of model-based interactive flower recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

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

[16]  Shenghuo Zhu,et al.  Image segmentation for large-scale subcategory flower recognition , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[17]  D. Anil Kumar,et al.  Continuous sign language recognition from tracking and shape features using Fuzzy Inference Engine , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[18]  P. V. V. Kishore,et al.  NEURAL NETWORK CLASSIFIER FOR CONTINUOUS SIGN LANGUAGE RECOGNITION WITH SELFIE VIDEO , 2017 .

[19]  M. Albuquerque,et al.  Multiscale Matching of Micro-CT images using Pattern Recognition and Hu moments , 2014 .

[20]  Vaegae Naveen Kumar,et al.  Development of an ANN-Based Pressure Transducer , 2016, IEEE Sensors Journal.

[21]  Ying Du,et al.  Egress Mechanism Color Image Segmentation Based on Region and Feature Fusion in Mars Exploration , 2017 .

[22]  Lei Wei,et al.  Texture aware image segmentation using graph cuts and active contours , 2013, Pattern Recognit..

[23]  D. Anil Kumar,et al.  Optical Flow Hand Tracking and Active Contour Hand Shape Features for Continuous Sign Language Recognition with Artificial Neural Networks , 2015, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[24]  Rosdiadee Nordin,et al.  Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling , 2016, IEEE Sensors Journal.

[25]  Jitendra Malik,et al.  Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation. , 2017, IEEE transactions on pattern analysis and machine intelligence.

[26]  P. V. V. Kishore,et al.  Conglomeration of Hand Shapes and Texture Information for Recognizing Gestures of Indian Sign Language Using Feed forward Neural Networks , 2013 .

[27]  E. Kiran Kumar,et al.  Training CNNs for 3-D Sign Language Recognition With Color Texture Coded Joint Angular Displacement Maps , 2018, IEEE Signal Processing Letters.

[28]  Namyong Kim,et al.  Euclidian distance minimization of probability density functions for blind equalization , 2010, Journal of Communications and Networks.

[29]  Wagner Coelho A. Pereira,et al.  Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound , 2012, IEEE Transactions on Medical Imaging.

[30]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[31]  N. Valliammal,et al.  Automatic Recognition System Using Preferential Image Segmentation For Leaf And Flower Images , 2011 .

[32]  E. Kiran Kumar,et al.  Motionlets Matching With Adaptive Kernels for 3-D Indian Sign Language Recognition , 2018, IEEE Sensors Journal.

[33]  Syed Inthiyaz,et al.  Pre-informed Level Set for Flower Image Segmentation , 2018 .

[34]  Syed Inthiyaz,et al.  FLOWER IMAGE SEGMENTATION: A COMPARISON BETWEEN WATERSHED, MARKER CONTROLLED WATERSHED, AND WATERSHED EDGE WAVELET FUSION , 2016 .

[35]  Andrew Zisserman,et al.  Delving deeper into the whorl of flower segmentation , 2010, Image Vis. Comput..

[36]  Kelly R. Thorp,et al.  Color image segmentation approach to monitor flowering in lesquerella , 2011 .

[37]  Kwang-Seok Hong,et al.  Mobile-Based Flower Recognition System , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[38]  D. S. Guru,et al.  Texture Features and KNN in Classification of Flower Images , 2010 .

[39]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Yong Gan,et al.  An active contour model based on fused texture features for image segmentation , 2015, Neurocomputing.