UJM TIV

UJM TIV Dataset Overview

Dataset Content

• Number of Classes: 11 material categories, including aluminium foil, brown bread, corduroy, cork, cotton, biscuits, lettuce leaf, linen, white bread, wood, and wool.
• Image Format: Each image is a 20 × 20 pixel RGB image.

Dataset Characteristics

• High Intra‑Class Variability: Provides images with substantial within‑class variation.
• Acquisition Conditions: Images were captured under controlled settings using four different viewpoints (front/90°, 10°, 30°, 60°) and four lighting conditions (front/90°, 20°, 45°, 65°) together with two sample orientations.

Intended Applications

• Research Purpose: Supports multi‑view learning solutions for material classification, employing a dual‑branch Siamese network that extracts and fuses information from two different viewpoints, achieving performance superior to conventional single‑view deep learning approaches.

Citation

• Reference: When using the UJM TIV dataset in scientific publications, please cite the following paper:
  • Multi‑View Learning for Material Classification

Contact

• Inquiry Email: borhancse.cu@gmail.com