Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.
Several other clustering methods have been considered for visual recognition. Leung and Malik used vector quantization in the Bag of Visual Words (BoVW) framework @cite_11 initially used for texture classification, but later adopted for other visual tasks. VLAD @cite_10 and Improved Fisher Vector @cite_16 encoding improved over hard vector quantization by including second order information in the descriptors. Fisher vector has been recently been used to achieved start-of-art performances on many data-sets @cite_29 .
@cite_29 @cite_16 @cite_10 @cite_11
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Bag of visual words (BoVW) models have been widely and successfully used in video based action recognition. One key step in constructing BoVW representation is to encode feature with a codebook. Recently, a number of new encoding methods have been developed to improve the performance of BoVW based object recognition and scene classification, such as soft assignment encoding [1], sparse encoding [2], locality-constrained linear encoding [3] and Fisher kernel encoding [4]. However, their effects for action recognition are still unknown. The main objective of this paper is to evaluate and compare these new encoding methods in the context of video based action recognition. We also analyze and evaluate the combination of encoding methods with different pooling and normalization strategies. We carry out experiments on KTH dataset [5] and HMDB51 dataset [6]. The results show the new encoding methods can significantly improve the recognition accuracy compared with classical VQ. Among them, Fisher kernel encoding and sparse encoding have the best performance. By properly choosing pooling and normalization methods, we achieve the state-of-the-art performance on HMDB51. The Fisher Vector (FV) representation of images can be seen as an extension of the popular bag-of-visual word (BOV). Both of them are based on an intermediate representation, the visual vocabulary built in the low level feature space. If a probability density function (in our case a Gaussian Mixture Model) is used to model the visual vocabulary, we can compute the gradient of the log likelihood with respect to the parameters of the model to represent an image. The Fisher Vector is the concatenation of these partial derivatives and describes in which direction the parameters of the model should be modified to best fit the data. This representation has the advantage to give similar or even better classification performance than BOV obtained with supervised visual vocabularies, being at the same time class independent. This latter property allows its usage both in supervised (categorization, semantic image segmentation) and unsupervised tasks (clustering, retrieval). In this paper we will show how it was successfully applied to these problems achieving state-of-the-art performances. Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required. This paper presents a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor with Whitening (RVD-W). It significantly advances the state of the art and delivers world-class performance. In our approach local descriptors are rank-assigned to multiple clusters. Residual vectors are then computed in each cluster, normalized using a direction-preserving normalization function and aggregated based on the neighborhood rank. Importantly, the residual vectors are de-correlated and whitened in each cluster before aggregation, leading to a balanced energy distribution in each dimension and significantly improved performance. We also propose a new post-PCA normalization approach which improves separability between the matching and non-matching global descriptors. This new normalization benefits not only our RVD-W descriptor but also improves existing approaches based on FV and VLAD aggregation. Furthermore, we show that the aggregation framework developed using hand-crafted SIFT features also performs exceptionally well with Convolutional Neural Network (CNN) based features. The RVD-W pipeline outperforms state-of-the-art global descriptors on both the Holidays and Oxford datasets. On the large scale datasets, Holidays1M and Oxford1M, SIFT-based RVD-W representation obtains a mAP of 45.1 and 35.1 percent, while CNN-based RVD-W achieve a mAP of 63.5 and 44.8 percent, all yielding superior performance to the state-of-the-art. A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. The most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements. This leads to the popular Bag-of-Visual words representation. In this work, we propose to use the Fisher Kernel framework as an alternative patch encoding strategy: we describe patches by their deviation from an "universal" generative Gaussian mixture model. This representation, which we call Fisher vector has many advantages: it is efficient to compute, it leads to excellent results even with efficient linear classifiers, and it can be compressed with a minimal loss of accuracy using product quantization. We report experimental results on five standard datasets--PASCAL VOC 2007, Caltech 256, SUN 397, ILSVRC 2010 and ImageNet10K--with up to 9M images and 10K classes, showing that the FV framework is a state-of-the-art patch encoding technique.