Food recognition using statistics of pairwise local features

Yang, Shulin; Chen, Mei; Pomerleau, Dean; Sukthankar, Rahul

doi:10.1109/cvpr.2010.5539907

Cited by 77 publications

(20 citation statements)

References 10 publications

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“…4(a) shows some sample images. For PFID database, we follow the experimental protocol in the published work [8,9] and perform 3-fold crossvalidation for our experiments, using the 12 images from two instances for training and the 6 images from the third for testing. This procedure is repeated three times by using a different instance serving as the test set and, average performance is calculated as the result.…”

Section: A Experimental Results On Pfid Datasetmentioning

confidence: 99%

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Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition

Kusumoto

Han

Chen

2014

2014 22nd International Conference on Pattern Recognition

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Recent year, with the increasing of unhealthy diets which will threaten people s life due to the various resulted risks such as heart stroke, liver trouble and so on, the maintaining for healthy life has attracted much attention and then how to manage the dietary life is becoming more and more important. In this research, we aim to construct an auto-recognition system of food images and keep the daily food-log records which will contribute to manage dietary life. With the easily available food images taken by mobile phone, it prospects to give the insight about the daily dietary of users with our constructed food recognition system. In order to achieve the acceptable recognition performance of the food images, we propose to apply a sparse model for coding local descriptors extracted from the food images and various pooling methods for aggregating the xoded descriptors. Sparse coding: an extension of vector quantization for local descriptors, which is popularly used in Bag-of-Features (BoF) for image representation, can reconstruct the local descriptors more effective, and then obtain more discriminated feature for food image representation. However, in order to emphasize the strongest activated pattern, the widely applied aggregation strategy of the sparse coded vector is only to retain the maximum coefficient in all (named as Max-pooling), which would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, we explore a hybrid aggregation strategy named as top-ranked average pooling (TRAP), which integrates not only the maximum activated magnitude but also the stronger activated number for image representation. Experiments validate that the proposed hybrid aggregation strategy combined with sparse model can greatly improve the recognition rates compared with the conventional BOF model and the state-of-the-art methods on two databases: our constructed RFID and the public PFID.

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Section: A Experimental Results On Pfid Datasetmentioning

confidence: 99%

“…The SPLF features by Shulin (Lynn) Yang etc. [9] can achieve the best performance 28% until 2010. The compared results are given in Fig.…”

Section: A Experimental Results On Pfid Datasetmentioning

confidence: 99%

Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition

Kusumoto

Han

Chen

2014

2014 22nd International Conference on Pattern Recognition

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“…In both cases, the same dataset of fast-food images were used. However, the authors of [5] rejected the concept of using SIFT local features or color histograms. Instead, they focused on features characterizing local texture properties of images.…”

Section: Vision-based Techniques For Food Recognitionmentioning

confidence: 99%

“…The works on the identification of fast-foods were continued in [5] and [6]. In both cases, the same dataset of fast-food images were used.…”

Section: Vision-based Techniques For Food Recognitionmentioning

confidence: 99%

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Machine Vision in Food Recognition: Attempts to Enhance CBVIR Tools

Sluzek

2016

Annals of Computer Science and Information Systems

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Abstract-Visual identification of complex images (e.g. images of food) remains a challenging problem. In particular, contentbased visual information retrieval (CBVIR) methods, which seem a natural choice for such tasks, are often constrained by specific characteristics of the images of interest and (possibly) other practical requirements. In this paper, a novel CBVIR approach to automatic food identification is proposed, taking into account characteristics of solutions currently existing in this area. Based on limitations of those solutions, we present a scheme in which a co-occurrence of MSER features extracted from three color channels is employed to build a bag-of-words histogram. Subsequently, food images are matched by detecting similarities between those histograms. Preliminary tests on a recently published benchmark dataset UNICT-FD889 reveal certain advantages of the scheme and highlight its limitations. In particular, a need of a novel methodology for segmentation of food images has been identified.

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DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

Liu

Cao

Luo

et al. 2016

Lecture Notes in Computer Science

198

110

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Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.

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Food recognition using statistics of pairwise local features

Cited by 77 publications

References 10 publications

Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition

Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition

Machine Vision in Food Recognition: Attempts to Enhance CBVIR Tools

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

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