Object detection and recognition via clustered features

Abstract The analysis of 2D images consists of two processes: detection and recognition of detected objects. Both stages allow for numerous applications in practical purposes, including detection of small objects and people with their appearance. The methods we can implement for these can benefit from fusion of approaches. In this article, we propose detection method based on analysis of the number of clusters of points in conjunction with Convolutional Neural Network as a final classifier. Proposed method of determining the clusters of points is based on a combination of modeled graphics processing with fuzzy logic. The proposed architecture of detection and classification has been tested and compared to other approaches in this field to show the efficiency and draw conclusions for further development.

[1]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[3]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Wenze Hu,et al.  Coupling-and-decoupling: A hierarchical model for occlusion-free object detection , 2014, Pattern Recognit..

[6]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Lei Zhang,et al.  Active Self-Paced Learning for Cost-Effective and Progressive Face Identification , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jian-Huang Lai,et al.  Discriminatively Trained And-Or Graph Models for Object Shape Detection , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Yizhou Yu,et al.  Automatic Photo Adjustment Using Deep Neural Networks , 2014, ACM Trans. Graph..

[11]  Yoram Yakimovsky,et al.  Boundary and Object Detection in Real World Images , 1974, JACM.

[12]  Luc Van Gool,et al.  Face Detection without Bells and Whistles , 2014, ECCV.

[13]  Horst Bunke,et al.  Inexact graph matching for structural pattern recognition , 1983, Pattern Recognit. Lett..

[14]  Donald B. Gennery,et al.  Object Detection and Measurement Using Stereo Vision , 1979, IJCAI.

[15]  Leonidas J. Guibas,et al.  Joint embeddings of shapes and images via CNN image purification , 2015, ACM Trans. Graph..

[16]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[17]  Yudong Zhang,et al.  Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine , 2015, Entropy.

[18]  Mohan M. Trivedi,et al.  Object detection based on gray level cooccurrence , 1984, Comput. Vis. Graph. Image Process..

[19]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[20]  Tania Stathaki,et al.  Car Detection in High-Resolution Urban Scenes Using Multiple Image Descriptors , 2014, 2014 22nd International Conference on Pattern Recognition.

[21]  Gui-Song Xia,et al.  Multi-object tracking with inter-feedback between detection and tracking , 2016, Neurocomputing.

[22]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[23]  Florent Perronnin,et al.  High-dimensional signature compression for large-scale image classification , 2011, CVPR 2011.

[24]  Deng Cai,et al.  Deep feature based contextual model for object detection , 2016, Neurocomputing.

[25]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[26]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[28]  Linda G. Shapiro,et al.  Unsupervised Template Learning for Fine-Grained Object Recognition , 2012, NIPS.

[29]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[30]  K. Sasaki,et al.  Learning to simplify , 2016, ACM Trans. Graph..

[31]  Shiguang Shan,et al.  Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness , 2016, Neurocomputing.

[32]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[33]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[34]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[35]  Nicu Sebe,et al.  Egocentric Daily Activity Recognition via Multitask Clustering , 2015, IEEE Transactions on Image Processing.

[36]  Steven Henikoff,et al.  SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..

[37]  Taku Komura,et al.  A Deep Learning Framework for Character Motion Synthesis and Editing , 2016, ACM Trans. Graph..

[38]  Huimin Lu,et al.  Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation , 2016, IEEE Access.