Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors

TL;DR

This paper introduces a novel Fisher vector encoding method based on sparse coding for high dimensional local features, significantly improving image classification performance over traditional GMM-based methods.

Contribution

It proposes a new model that replaces GMM with a subspace-based Gaussian, enabling sparse coding for high dimensional features, and demonstrates its effectiveness with CNN descriptors.

Findings

Outperforms traditional GMM Fisher vectors in accuracy

Achieves state-of-the-art results in various image classification tasks

Efficiently models high dimensional features with sparse coding

Abstract

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 characterize the generation process of local features. This choice has shown to be sufficient for traditional low dimensional local features, e.g., SIFT; and typically, good performance can be achieved with only a few hundred Gaussian distributions. However, the same number of Gaussians is insufficient to model the feature space spanned by higher dimensional local features, which have become popular recently. In order to improve the modeling capacity for high dimensional features, it turns out to be inefficient and computationally impractical to simply increase the number of Gaussians. In this paper, we propose a model in which each local feature is drawn from a Gaussian distribution whose mean vector is sampled from a subspace. With certain approximation, this model can be converted to a sparse coding procedure and the learning/inference problems can be readily solved by standard sparse coding methods. By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently developed Deep Convolutional Neural Network (CNN) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC. Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition, indoor scene, and fine-grained image classification problems.