ABSTRACT This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.

中文翻译：

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使用神经网络分类器和多假设图像分割的食物识别

摘要 本文提出了用于食物识别的多假设图像分割和前馈神经网络分类器以提高性能。最初，食物或膳食图像作为输入给出。然后，使用显着区域检测、多尺度分割和快速拒绝，应用分割来识别特定食物所在的区域。然后，通过全局特征和局部特征提取来提取每个食物项的特征。获得特征后，使用前馈神经网络模型对每个分割区域进行分类。最后，在 (i) 食物量和 (ii) 基于质量值的卡路里和营养测量的帮助下计算卡路里值。验证了实验结果和性能评估。