In this project I evaluated the performance of a number of popular object recognition algorithms, with differing levels of complexity, when tested against SHORT. One of the main motivations was to devise the open challenges in the visual object recognition of objects that are being held by users and to establish a baseline for SHORT to be compared against other datasets.
Benchmarked methods: SIFT descriptor matching, SIFT bag of words (BOW) model with different encoding techniques: hard assignment (HA), locality constrained linear coding (LLC) and Fisher Vectors.
Benchmarked datasets: VOC 2007, Caltech-101, SHORT.
Keywords: Wearable cameras, assistive context, open databases, object recognition and retrieval, classification, SVM, Fisher Vectors, VLAD, GMM, VLFEAT, descriptors, bag-of-words, encoding methods, open source code.
More information soon…