COMPARISON OF IMAGE CLASSIFICATION TECHNIQUES USING CALTECH 101 DATASET

By N. Kamarudin, M. Makhtar, S. A. Fadzli, M. Mohamad, F. Mohamad, M. Abdul. 2015

17 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. A. Hanna
2. Emily L. Denton
3. Inioluwa Deborah Raji
4. Amandalynne Paullada
5. Emily M. Bender
6. Luc Van Gool
7. Ming-Hsuan Yang
8. Dacheng Tao

In Evaluation & Benchmarks

1. Emily L. Denton
2. A. Hanna
3. Inioluwa Deborah Raji
4. Emily M. Bender
5. Amandalynne Paullada
6. Alexandra Sasha Luccioni
7. Bertie Vidgen
8. Ben Hutchinson

Citations in the corpus

• AI and the Everything in the Whole Wide World Benchmark (2021, background): “…it offered 20 times the number of categories, and 100 times the number of total images as the most popular training and evaluation benchmarks at the time, such as Caltech 101/256 [Kamarudin et al., 2015, Griffin et al., 2007], MSRC [Shotton et al., 2006] and PASCAL VOC [Everingham et al., 2010].”