Transcript Classification of the aesthetic value of images based on histogram

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
Goal:
Assess feasibility of developing an aesthetic label classifier
for abstract images generated by RMIT’s Imagene software.
VS.
Interesting
Not Interesting
Image Classification
• Classification Algorithms
– Support Vector Machines
– Random Committee ensemble algorithm
• Random Forest Base classifier
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).
Software
• Image Generation
– RMIT’s Imagene 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
Methodology
1. Generate 5 Image test sets with RMIT’s Imagene
software. Move each image into either interesting or
not interesting directories.
2. Extract features from each image via the GNU Image
Finding Tool (GIFT).
3. Unpack binary feature files for each image and merge
them into a feature matrix (CSV).
4. Cut down feature matrix to the colour and Gabor
histogram attributes.
5. Import histogram feature matrix into WEKA and train
SMO and Random Committee classifiers via 10-fold
Cross Validation.
Results – Imagene Images
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.