Naslov
Multi-Label Classification of Traffic Scenes

Autori
Sikirić, Ivan ; Brkić, Karla ; Horvatin, Ivan ; Šegvić, Siniša

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

Izvornik
CCVW 2014 Proceedings of the Croatian Computer Vision Workshop / Lončarić, Sven ; Subašić, Marko - Zagreb : Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2014, 9-14

Skup
3rd Croatian Computer Vision Workshop

Mjesto i datum
Zagreb, Hrvatska, 16.09.2014

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Random Forest; bag-of-words; GIST; SIFT; k-means; feature point extraction

Sažetak
This work deals with multi-label classification of traffic scene images. We introduce a novel labeling scheme for the traffic scene dataset FM2. Each image in the dataset is assigned up to five labels: settlement, road, tunnel, traffic and overpass. We propose representing the images with (i) bag-of-words and (ii) GIST descriptors. The bag-of-words model detects SIFT features in training images, clusters them to form visual words, and then represents each image as a histogram of visual words. On the other hand, the GIST descriptor represents an image by capturing perceptual features meaningful to a human observer, such as naturalness, openness, roughness, etc. We compare the two representations by measuring classification performance of Support Vector Machine and Random Forest classifiers. Labels are assigned by applying binary one-vs-all classifiers trained separately for each class. Categorization success is evaluated over multiple labels using a variety of parameters. We report good classification results for easier class labels (road, F1 = 98% and tunnel, F1 = 94%), and discuss weaker results (overpass, F1 < 50%) that call for use of more advanced methods.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA