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G oal: Assess feasibility of developing an aesthetic label classifier for abstract images generated by RMIT’s Imagene

Classification of the aesthetic value of images based on histogram features By Xavier Clements & Tristan Penman Supervisors : Vic Ciesielski , Xiadong Li Acknowledgment: Rahayu Binti A Hamid. G oal:

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G oal: Assess feasibility of developing an aesthetic label classifier for abstract images generated by RMIT’s Imagene

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  1. Classification of the aesthetic value of images based on histogram featuresBy Xavier Clements & Tristan PenmanSupervisors: Vic Ciesielski, Xiadong Li Acknowledgment: RahayuBinti A Hamid Goal: Assess feasibility of developing an aesthetic label classifier for abstract images generated by RMIT’sImagene software. VS. Interesting Not Interesting

  2. Image Classification • Classification Algorithms • Support Vector Machines • Random Committee ensemble algorithm • Random Forest Base classifier

  3. Computational Aesthetics / Histogram Features • Computational Aesthetics • is the analysis of image sets for their aesthetic value. • Yeowen Wu et al study - “The good, the bad, and the ugly: Predicting aesthetic image labels” - 2010 • Histograms • The use of histogram features was chosen as a way of attaining a global description of each image, this method having been employed successfully in previous studies (Chapelle et al 1999).

  4. Software • Image Generation • RMIT’sImagene System • Feature Extraction • GNU Image Finding Tool (GIFT) • Data Mining • WEKA 3 Data Mining Software suite (WEKA) • Sequential Minimum Optimization (SMO) algorithm • Random Committee with Random Forest base (RCRF)classifier

  5. Methodology • Generate 5 Image test sets with RMIT’sImagene software. Move each image into either interesting or not interesting directories. • Extract features from each image via the GNU Image Finding Tool (GIFT). • Unpack binary feature files for each image and merge them into a feature matrix (CSV). • Cut down feature matrix to the colour and Gabor histogram attributes. • Import histogram feature matrix into WEKA and train SMO and Random Committee classifiers via 10-fold Cross Validation.

  6. Results – Imagene Images

  7. Results / Conclusions • Random Committee with Random Forest Base Classifier (RCRF) - 94.52% • SMO - 93.57% • RCRF outperformed the SMO in overall classification, as well as having higher precision and recall values for the interesting class. • Conclusion: • The higher than expected classification accuracies ensure that a classifier (RCRF or SMO) can be used to delineate relatively accurately between interesting and not interesting Imagene images.

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