Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models. However, it remains unclear how these models can capt
ure complex appearance variations using visual codebooks of limited sizes and coarse geometric information. In this work, we propose to interpret Fisher-Vector-based object detectors as part-based models. Through the use of several visualizations and experiments, we show that this is a useful insight to explain the good performance of the model. Furthermore, we reveal for the first time several interesting properties of the FV, including its ability to work well using only a small subset of input patches and visual words. Finally, we discuss the relation of the FV and DPM detectors, pointing out differences and commonalities between them.
A natural approach to teaching a visual concept, e.g. a bird species, is to show relevant images. However, not all relevant images represent a concept equally well. In other words, they are not necessarily iconic. This observation raises three questi
ons. Is iconicity a subjective property? If not, can we predict iconicity? And what exactly makes an image iconic? We provide answers to these questions through an extensive experimental study on a challenging fine-grained dataset of birds. We first show that iconicity ratings are consistent across individuals, even when they are not domain experts, thus demonstrating that iconicity is not purely subjective. We then consider an exhaustive list of properties that are intuitively related to iconicity and measure their correlation with these iconicity ratings. We combine them to predict iconicity of new unseen images. We also propose a direct iconicity predictor that is discriminatively trained with iconicity ratings. By combining both systems, we get an iconicity prediction that approaches human performance.
