iFRAME (inhomogeneous Filters Random Field And Maximum Entropy)

Experiment 3.2: Clustering by fitting Mixture of iFRAME models without local shift (soft EM version)

Clustering by EM-like algorithm by fitting Mixture of iFRAME Models without local shift. The clustering is model-based, which does NOT require or rely on pairwise similarity meausre. The clustering uses soft classification.
Three characteristics: (1) Multiple scales of Gabor features for good representation; (2) Multiple chains of HMC sampling for accurate parameters estimation, (3) Multiple GPUs of parallel computating for fast computation.

Code and dataset

Experiment 3.1: hard EM version
Experiment 3.3: DoG version

Contents
Case 1: Horses facing two different directions
Case 2: Horses and zebras
Case 3: Human faces, butterflies, and horses
Case 4: Cars, motorbikes, scooters, and bikes

Case 1: Horses facing two different directions (details)

templates

Synthesized templates by multiple chains

Case 2: Horses and zebras (details)

templates

Synthesized templates by multiple chains

Case 3: Human faces, butterflies, and horses (details)

templates

Synthesized templates by multiple chains

Case 4: Cars, motorbikes, scooters, and bikes (details)

templates

Synthesized templates by multiple chains