Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

TL;DR

This paper introduces a local modeling approach for deep CNN features to enhance fine-grained image classification, involving dimensionality reduction and Gaussian mixture modeling, leading to improved performance on specific datasets.

Contribution

It presents a novel method combining dimensionality reduction and local feature modeling in CNNs for better fine-grained classification accuracy.

Findings

Significant performance gains on Fish dataset

Improved results on UEC FOOD-100 dataset

Effective use of low-dimensional features for modeling

Abstract

We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.