Naslov
Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification with Large Fisher Vectors

Autori
Krapac, Josip ; Šegvić, Siniša

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Pattern Recognition ; 37th German Conference, GCPR 2015 Aachen, Germany, October 7–10, 2015 Proceedings. Lecture Notes in Computer Science vol. 9358, ISSN 0302-974. / Gall, Juergen ; Gehler, Peter ; Leibe, Bastian - Cham : Springer, 2015, 470-480

ISBN
978-3-319-24946-9

Skup
German Conference on Pattern Recognition

Mjesto i datum
Aachen, Njemačka, 07.10.2015. - 10.10.2015

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
GMM soft-assign; Fisher vectors.

Sažetak
We address two drawbacks of image classification with large Fisher vectors. The first drawback is the computational cost of assigning a large number of patch descriptors to a large number of GMM components. We propose to alleviate that by a generally applicable approximate soft-assignment procedure based on a balanced GMM tree. This approximation significantly reduces the computational complexity while only marginally affecting the fine-grained classification performance. The second drawback is a very high dimensionality of the image representation, which makes the classifier learning and inference computationally complex and prone to overtraining. We propose to alleviate that by regularizing the classification model with group Lasso. The resulting block-sparse models achieve better fine-grained classification performance in addition to memory savings and faster prediction. We demonstrate and evaluate our contributions on a standard fine-grained categorization benchmark.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA