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Food being the vital part of everyone's lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-VectorMachine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier.

Abstract-With the generation of massive amount of productcentric responses from existing applications on collaborative platform, it is necessary to perform a discrete analytical operation on it. As majority of such responses are textual in nature, it increases the applicability of using text mining approaches on it. We review the existing research contribution in text mining to find that there are significant research gap. Therefore, the proposed study presents a technique called as RKE-CP i.e. Response-based Knowledge Extraction from Collaborative Platform where the term Collaborative points towards cloud environment. The proposed technique is designed using mathematical modelling where the maximum focus of design and implementation lies on accomplishing a good balance between faster response time in mining operation and higher precision/recall rate. The study outcome possess' better precision score, recall, and lowered processing time as compared with the most relevant work text mining.