Compositional Model based Fisher Vector Coding for Image Classification

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to depict the generation process of local features. However, the representative power of the GMM could be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes and the number of prototypes is usually small in FVC. To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions. Instead, we adopt a compositional mechanism which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as the linear combination of multiple key components and the combination weight is a latent random variable. In this way, we can greatly enhance the representative power of the generative model of FVC. To implement our idea, we designed two particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and Machine Intelligence (TPAMI

Generalized Max Pooling

State-of-the-art patch-based image representations involve a pooling operation that aggregates statistics computed from local descriptors. Standard pooling operations include sum- and max-pooling. Sum-pooling lacks discriminability because the resulting representation is strongly influenced by frequent yet often uninformative descriptors, but only weakly influenced by rare yet potentially highly-informative ones. Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions. We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector -- hence the name Generalized Max Pooling (GMP). It involves equalizing the similarity between each patch and the pooled representation, which is shown to be equivalent to re-weighting the per-patch statistics. We show on five public image classification benchmarks that the proposed GMP can lead to significant performance gains with respect to heuristic alternatives.Comment: (to appear) CVPR 2014 - IEEE Conference on Computer Vision & Pattern Recognition (2014

Machine learning solutions to visual recognition problems

This thesis gives an overview of my research since my arrival in December 2005 as a postdoctoral fellow at the in the LEAR team at INRIA Rhone-Alpes. After a general introduction in Chapter 1, the contributions are presented in chapters 2–4 along three themes. In each chapter we describe the contributions, their relation to related work, and highlight two contributions with more detail. Chapter 2 is concerned with contributions related to the Fisher vector representation. We highlight an extension of the representation based on modeling dependencies among local descriptors (Cinbis et al., 2012, 2016a). The second highlight is on an approximate normalization scheme which speeds-up applications for object and action localization (Oneata et al., 2014b). In Chapter 3 we consider the contributions related to metric learning. The first contribution we highlight is a nearest-neighbor based image annotation method that learns weights over neighbors, and effectively determines the number of neighbors to use (Guillaumin et al., 2009a). The second contribution we highlight is an image classification method based on metric learning for the nearest class mean classifier that can efficiently generalize to new classes (Mensink et al., 2012, 2013b). The third set of contributions, presented in Chapter 4, is related to learning visual recognition models from incomplete supervision. The first highlighted contribution is an interactive image annotation method that exploits dependencies across different image labels, to improve predictions and to identify the most informative user input (Mensink et al., 2011, 2013a). The second highlighted contribution is a multi-fold multiple instance learning method for learning object localization models from training images where we only know if the object is present in the image or not (Cinbis et al., 2014, 2016b). Finally, Chapter 5 summarizes the contributions, and presents future research directions

Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns

Accepted for publication in International Journal of Computer Vision and Image Understanding (CVIU)International audienceWe propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that this leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance

Image Classification with the Fisher Vector: Theory and Practice

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 (BOV) 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 (FV) 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