Beer Label Classification for Mobile Applications
نویسندگان
چکیده
We present an image processing algorithm for the automated identification of beer types using SIFT-based image matching of bottle labels. With a database of 100 beer labels from various breweries, our algorithm correctly matched 100% of corresponding query photographs with an average search time of 11 seconds. To test the sensitivity of our algorithm, we also collected and tested a second database of 30 labels from the same brewery. Remarkably, the algorithm still correctly classified 97% of labels. In addition to these results, we show that the SIFT-based recognition system is highly robust against camera motion and camera-to-bottle distance.
منابع مشابه
Exploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملClassification of encrypted traffic for applications based on statistical features
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applicat...
متن کاملReduction of Energy Consumption in Mobile Cloud Computing by Classification of Demands and Executing in Different Data Centers
In recent years, mobile networks have faced with the increase of traffic demand. By emerging mobile applications and cloud computing, Mobile Cloud Computing (MCC) has been introduced. In this research, we focus on the 4th and 5th generation of mobile networks. Data Centers (DCs) are connected to each other by high-speed links in order to minimize delay and energy consumption. By considering a ...
متن کاملارائه یک رویکرد همانند سازی شده عامل محور در اجرای یک الگوی کد متحرک مطمئن
Abstract Using mobile agents, it is possible to bring the code close to the resources, which is not foreseen by the traditional client/server paradigm. Compared to the client/server computing paradigm, the greater flexibility of the mobile agent paradigm comes at additional costs as well as the additional complexity of developing and managing mobile agent-based applications. Such complexity ...
متن کامل