Moray Allan

The 2005 PASCAL visual object classes challenge

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

Improving object classification using semantic attributes

This paper shows how semantic attribute features can be used to improve object classification performance. The semantic attributes used fall into five groups: scene (e.g. ‘road’), colour (e.g. ‘green’), part (e.g. ‘face’), shape (e.g. ‘box’), and material (e.g. ‘wood’). We train classifiers from representative images for 60 semantic attributes. We first assess the accuracy of the individual classifiers, and show that they can be used to predict semantic annotations for test images. We then use output from the set of trained classifiers to create a new low-dimensional image representation. Experiments on data from the PASCAL VOC challenge show that the semantic attribute features achieve an object classification performance close to that of high-dimensional bag-of-words features, and that using a combination of semantic attribute features and bag-of-words features gives a better classification performance than using either feature set alone.

Fast Learning of Sprites using Invariant Features.

A popular framework for the interpretation of image sequences is the layers or sprite model of e.g. Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretized transformations (e.g. translations, or affines) for each layer. In this paper we show that by using invariant features (e.g. Lowe’s SIFT features) and clustering their motions we can reduce or eliminate the search and thus learn the sprites much faster. We demonstrate our algorithm on two image sequences.