Wide-Slice Residual Networks for Food Recognition (1612.06543v1)

Abstract: Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. Specifically, inspired by the recent success of residual deep network, we exploit such a learning scheme and introduce a slice convolution block to capture the vertical food layers. Outputs of the deep residual blocks are combined with the sliced convolution to produce the classification score for specific food categories. To evaluate our proposed architecture we have conducted experimental results on three benchmark datasets. Results demonstrate that our solution shows better performance with respect to existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).

• The paper introduces the novel WISeR architecture that integrates wide residual and slice branches to tackle food-specific classification challenges.
• It employs a wide residual network to enhance feature representation and slice convolutions to capture vertical food structures, achieving a top-1 accuracy of 90.27% on Food-101.
• This approach offers practical benefits for dietary monitoring and inspires further research on domain-specific deep learning models.

Analysis of Wide-Slice Residual Networks for Food Recognition

The paper "Wide-Slice Residual Networks for Food Recognition" presents a novel approach to the challenging problem of food recognition using deep neural networks. The authors introduce the WISeR architecture, which is specifically tailored to handle the structural peculiarities of food images. The architecture innovatively combines the power of residual learning with a proposed slice convolutional layer to address the intricate features of different food dishes.

In the field of food recognition, intra-class variation presents significant challenges. Individual food categories may exhibit considerable variability depending on preparation methods, ingredients, and visual presentation, which complicates traditional classification tasks. The paper underscores the need for a custom network architecture that leverages specific characteristics of food items, such as their spatial structure, which has not been thoroughly considered by standard deep learning models typically employing off-the-shelf architectures.

The core component of the proposed approach is the Wide-Slice Residual Network (WISeR). The architecture is composed of two principal branches: a wide residual network branch and a slice network branch. The wide residual branch employs an extensive number of feature maps in each convolutional layer, enhancing the representational capability of the network by countering the diminishing feature reuse issue seen in conventional deep networks. This approach capitalizes on residual learning methodologies to maintain the integrity of features across deeper layers, thus ensuring robust feature extraction.

The slice network branch is designed to address the vertical food structure characteristic of many dishes. By using slice convolution layers, the branch captures vertical layering within food items, offering a distinctive advantage in recognizing foods based on their intrinsic assembly. This intentional architectural feature demonstrates the authors' focus on food-specific image attributes, enhancing the network's ability to discern and classify complex food images accurately.

Experimental evaluations of the WISeR architecture were conducted on three prominent benchmark datasets: UECFood100, UECFood256, and Food-101. The architecture shows substantial performance improvements over existing methodologies, particularly outperforming traditional CNN-based approaches that do not account for food-specific structures. WISeR demonstrates an impressive top-1 accuracy of 90.27% on the challenging Food-101 dataset, indicative of the model’s efficacy in generalizing across a wide range of food classes.

The implications of this research are notable for both practical applications and theoretical advancements in AI. Practically, the development of precise food recognition systems could immensely benefit dietary monitoring applications, contributing to public health by facilitating accurate dietary tracking and nutritional assessment. Theoretically, the paper highlights the value in designing problem-specific neural network architectures, particularly in domains where context-specific features (such as food structure) play a critical role in classification tasks.

Looking ahead, the methodology adopted in this paper could spur additional research into dense semantic network architectures tailored to other domains with specific structural attributes. Future work might explore further optimizations to the WISeR architecture, reducing computational demand for potential deployment on mobile and edge devices, thus broadening its applicability in real-world scenarios. Additionally, further exploration into interpretability provisions and enhancements in slice convolutional capture could bolster the capacity of deep learning frameworks to offer explainable AI solutions in diverse applied settings.