Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

TL;DR

This paper introduces a novel CNN-based method for selfie detection that leverages synergy features from head and shoulder orientations, using CCA to improve discrimination in large image datasets.

Contribution

The paper proposes a new synergy-constraint based CNN approach for selfie detection, combining LBP, HOG, and CCA to enhance feature discrimination over existing architectures.

Findings

Proposed method outperforms popular CNN architectures like GoogleNet and AlexNet.

Effective in large datasets with 90,000 images containing equal selfies and non-selfies.

Captures subtle image features for improved selfie classification.

Abstract

Categorisation of huge amount of data on the multimedia platform is a crucial task. In this work, we propose a novel approach to address the subtle problem of selfie detection for image database segregation on the web, given rapid rise in number of selfies clicked. A Convolutional Neural Network (CNN) is modeled to learn a synergy feature in the common subspace of head and shoulder orientation, derived from Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) features respectively. This synergy was captured by projecting the aforementioned features using Canonical Correlation Analysis (CCA). We show that the resulting network's convolutional activations in the neighbourhood of spatial keypoints captured by SIFT are discriminative for selfie-detection. In general, proposed approach aids in capturing intricacies present in the image data and has the potential for usage in other subtle image analysis scenarios apart from just selfie detection. We investigate and analyse the performance of popular CNN architectures (GoogleNet, AlexNet), used for other image classification tasks, when subjected to the task of detecting the selfies on the multimedia platform. The results of the proposed approach are compared with these popular architectures on a dataset of ninety thousand images comprising of roughly equal number of selfies and non-selfies. Experimental results on this dataset shows the effectiveness of the proposed approach.