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@article{146334,
        author = {SUBHASHINI. T and Chandra Prabha. K},
        title = {FOOD RECOGNITION SYSTEM FOR DIABETIC PATIENT USING IMAGE COMPARISON },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {192-196},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146334},
        abstract = {In this way, the feature vector clustering procedure can be omitted; however, less information is considered by the model which might not be able to deal with high visual variation. Moreover, the proposed system requires a colored checker-board captured within the image in order to deal with varying lighting conditions. In an independently collected dataset, the system achieved accuracies from 95% to 80%, as the number of food categories increases from   2 to 20. The last few years food recognition has attracted a lot of attention for dietary assessment,most of the proposed systems fail to deal with the problem of the huge visual diversity of   foods,so they limit the visual dataset considered to either too few or too narrow food classes, in order to achieve satisfactory results.  The present study makes several contributions to the field of food recognition. A visualdataset with nearly 5000 homemade food images was created, reflecting the nutritional habits incentral Europe. The foods   appearing in the images have been organized into 11 classes of high intra variability. Based on the aforementioned dataset, we conducted an extensive investigation further optimal components and parameters within the Bag Of Feature architecture. It is applicable for all places such as Hospitals,Restaurants and anywhere else},
        keywords = {Food recognition, diabetic analysis using image comparison},
        month = {},
        }