Dimension Reduction Using Image Transform for Content-Based Feature Extraction

Technological advancements in the field of machine learning have attempted classification of the images of gigantic datasets. Classification with content-based image feature extraction categorizes the images based on the image content in contrast to conventional text-based annotation. The chapter has presented a feature extraction technique based on application of image transform. The method has extracted meaningful features and facilitated feature dimension reduction. A technique, known as fractional coefficient of transforms, is adopted to facilitate feature dimension reduction. Two different color spaces, namely RGB and YUV, are considered to compare the classification metrics to figure out the best possible reduced feature dimension. Further, the results are compared to state-of-the-art techniques which have revealed improved performance for the proposed feature extraction technique. Dimension Reduction Using Image Transform for ContentBased Feature Extraction

[1]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[2]  Saurav Ghosh,et al.  Decision fusion-based approach for content-based image classification , 2017, Int. J. Intell. Comput. Cybern..

[3]  Caixia Liu.  A new finger vein feature extraction algorithm , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[4]  Jing-Ming Guo,et al.  Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Jing-Ming Guo,et al.  Improved Block Truncation Coding Using Optimized Dot Diffusion , 2014, IEEE Transactions on Image Processing.

[6]  Ljubomir J. Buturovic,et al.  Cross-validation pitfalls when selecting and assessing regression and classification models , 2014, Journal of Cheminformatics.

[7]  David Zhang,et al.  A face and palmprint recognition approach based on discriminant DCT feature extraction , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Li Li,et al.  A new approach to chaotic image encryption based on quantum chaotic system, exploiting color spaces , 2013, Signal Process..

[9]  Aleksandra Kawala-Janik,et al.  YUV vs RGB-Choosing a Color Space for Human-Machine Interaction , 2014, FedCSIS.

[10]  Yang Li,et al.  Combining Nonlinear Dimension Reduction and Hashing Method for Efficient Image Retrieval , 2016, 2016 12th International Conference on Semantics, Knowledge and Grids (SKG).

[11]  Huei-Yung Lin,et al.  Performance Evaluation of Bit-plane Slicing based Stereo Matching Techniques , 2015, VISAPP.

[12]  Saurav Ghosh,et al.  A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification , 2014 .

[13]  Saurav Ghosh,et al.  Content Based Image Recognition by Information Fusion with Multiview Features , 2015 .

[14]  Sudeep D. Thepade,et al.  Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers , 2013, 2013 Annual IEEE India Conference (INDICON).

[15]  Jing-Ming Guo,et al.  Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding , 2015, IEEE Transactions on Image Processing.

[16]  Douglas N Rutledge,et al.  PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. , 2015, Molecular bioSystems.

[17]  Yang Yanli,et al.  A novel local threshold binarization method for QR image , 2012 .

[18]  Deepanwita Das.  A Distributed Algorithm for Un-balanced Partitioning of a Swarm of Autonomous Mobile Robots and Its Performance Analysis , 2012 .

[19]  Saurav Ghosh,et al.  Decision Fusion for Classification of Content Based Image Data , 2017, Trans. Comput. Sci..

[20]  U. Rajendra Acharya,et al.  Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features , 2012, Knowl. Based Syst..

[21]  Rik Das,et al.  Partition selection with sparse autoencoders for content based image classification , 2017, Neural Computing and Applications.

[22]  P. Ganesan,et al.  Assessment of satellite image segmentation in RGB and HSV color space using image quality measures , 2014, 2014 International Conference on Advances in Electrical Engineering (ICAEE).

[23]  Yong-Hwan Lee,et al.  Content-based image retrieval using spatial-color and Gabor texture on a mobile device , 2013, Comput. Sci. Inf. Syst..

[24]  Aman Pal,et al.  An Effective and Fast Hybrid Framework for Color Image Retrieval , 2014 .

[25]  Nabendu Chaki,et al.  A new image binarization method using iterative partitioning , 2012, Machine Vision and Applications.

[26]  Diana Inkpen,et al.  uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[27]  Vadlamani Ravi,et al.  A survey on opinion mining and sentiment analysis: Tasks, approaches and applications , 2015, Knowl. Based Syst..

[28]  Li Chen,et al.  Recommendation Based on Contextual Opinions , 2014, UMAP.

[29]  Rik Das,et al.  A Novel Feature Extraction Technique for Content Based Image Classification in Digital Marketing Platform , 2015 .

[30]  Piotr Tokarczyk,et al.  Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.