Food image segmentation using edge adaptive based deep-CNNs

Abstract

Purpose

Indian food recognition can be considered as a case of fine-grained type visual recognition, where the several photos of same category generally have significant variability. Therefore, effective segmentation and classification technique is required to identify the particular cuisines and fine-grained analysis. The paper aims to discuss this issue.

Design/methodology/approach

In this paper, the authors provided an effective segmentation approach through the proposed edge adaptive (EA)-deep convolutional neural networks (DCNNs) model, where each input images are divided into patches in order to provide much efficient and accurate structural description of data.

Findings

EA-DCNNs starts with developing a coarse map of feature that obtained through DCNN, afterwards EA model is applied to construct the final segmented image.

Originality/value

The training model of EA-DCNN consists of pooling, rectified linear unit and convolution, which help convolutional network to optimize the performance of segmentation in a significant extent, which is much practical and relevant in the context of food image segmentation.

Citation

C. Burkapalli, V. and Patil, P.C. (2020), "Food image segmentation using edge adaptive based deep-CNNs", International Journal of Intelligent Unmanned Systems, Vol. 8 No. 4, pp. 243-252. https://doi.org/10.1108/IJIUS-09-2019-0053