Stability and reduction of statistical features for image classification and retrieval: Preliminary results

Content-based image retrieval (CBIR) and image classification are challenging problems in computer vision. In both fields, feature extraction plays an important role in ensuring effectiveness and stability of the results. In this paper, we present an experimental study to test the robustness of the features we proposed earlier. We demonstrate that the extraction of statistical features locally using small block size and different color models makes the features more robust and stable. The features perform well under dimensionality reduction and for different validation approaches and several classification algorithms. Discrete wavelet transform (DWT) is used for dimensionality reduction and re-sampling. The experimental results show that the extracted features are stable enough for classification and the classifiers perform well after dimensionality reduction. In addition to reducing the training time significantly while maintaining almost the same system performance, and therefore, such statistical features can be efficiently used for CBIR and image classification.

[1]  Ahmad B. A. Hassanat,et al.  Visual Speech Recognition , 2011, ArXiv.

[2]  William M. Wells,et al.  Medical Image Analysis - past, present, and future , 2016, Medical Image Anal..

[3]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[4]  Swati Agarwal,et al.  Content Based Image Retrieval using Color Edge Detection and Discrete Wavelet Transform , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[5]  Praveen Bhanodiya,et al.  An Effective Content-Based Image Retrieval Using Color, Texture and Shape Feature , 2013, ICACNI.

[6]  Xiangyang Wang,et al.  Content-based image retrieval by integrating color and texture features , 2012, Multimedia Tools and Applications.

[7]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[8]  John P. Eakins,et al.  Towards intelligent image retrieval , 2002, Pattern Recognit..

[9]  Muhammad Kamal Hossen,et al.  A surveillance system based on motion detection and motion estimation using optical flow , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[10]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Md Kamrul Hasan,et al.  EMD‐DWT based transform domain feature reduction approach for quantitative multi‐class classification of breast lesions , 2017, Ultrasonics.

[12]  Gwénolé Quellec,et al.  Deep image mining for diabetic retinopathy screening , 2016, Medical Image Anal..

[13]  Ardhendu Tripathy,et al.  Dimensionality Reduction of Data Warehouse Using Wavelet Transformation: An Enhanced Approach for Business Process , 2011 .

[14]  Mong-Li Lee,et al.  Image Mining: Trends and Developments , 2002, Journal of Intelligent Information Systems.

[15]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Mahmoud B. Alhasanat,et al.  Victory sign biometrie for terrorists identification: Preliminary results , 2017, 2017 8th International Conference on Information and Communication Systems (ICICS).

[17]  Shubha. G. Sanu,et al.  Satellite Image Mining using Content Based Image Retrieval , 2017 .

[18]  A. Hassanat,et al.  FUSION OF COLOR AND STATISTIC FEATURES FOR ENHANCING CONTENT-BASED IMAGE RETRIEVAL SYSTEMS 1 , 2016 .

[19]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Zhihua Xia,et al.  A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing , 2016, IEEE Transactions on Information Forensics and Security.

[21]  V. B. Surya Prasath,et al.  Classification and gender recognition from veiled-faces , 2017, Int. J. Biom..