Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation

TL;DR

This paper introduces hybrid Gaussian-Laplacian mixture models to derive Fisher Vectors, improving image annotation and search performance by better modeling descriptor distributions.

Contribution

It presents a novel Hybrid Gaussian-Laplacian Mixture Model and derives Fisher Vectors from it, outperforming traditional GMM-based methods.

Findings

State-of-the-art results in image annotation

Enhanced image search accuracy

Effective modeling of descriptor distributions

Abstract

In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks.