Abstract
The cutting-edge technology Machine Learning (ML) is successfully applied for Business Intelligence. Among the various pre-processing steps of ML, Automatic Image Annotation (also known as automatic image tagging or linguistic indexing) is the process in which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. Automatic Image Annotation (AIA) methods (which have appeared during the last several years) make a large use of many ML approaches. Clustering and classification methods are most frequently applied to annotate images. In addition, these proposed solutions require a high computational infrastructure. However, certain real-time applications (small and ad-hoc intelligent applications) for example, autonomous small robots, gadgets, drone etc. have limited computational processing capacity. These small and ad-hoc applications demand a more dynamic and portable way to automatically annotate data and then perform ML tasks (Classification, clustering etc.) in real time using limited computational power and hardware resources. Through a comprehensive literature study we found that most image pre-processing algorithms and ML tasks are computationally intensive, and it can be challenging to run them on an embedded platform with acceptable frame rates. However, Raspberry Pi is sufficient for AIA and ML tasks that are relevant to small and ad-hoc intelligent applications. In addition, few critical intelligent applications (which require high computational resources, for example, Deep Learning using huge dataset) are only feasible to run on more powerful hardware resources. In this study, we present the framework of “Automatic Image Annotation for Small and Ad-hoc Intelligent Application using Raspberry Pi” and propose the low-cost infrastructures (single node and multi node using Raspberry Pi) and software module (for Raspberry Pi) to perform AIA and ML tasks in real time for small and ad-hoc intelligent applications. The integration of both AIA and ML tasks in a single software module (with in Raspberry Pi) is challenging. This study will helpful towards the improvement in various practical applications areas relevant to small intelligent autonomous systems.
Highlights
The state-of-art Machine Learning (ML) algorithms are contributing to several sectors of human life through Big Data [01]
The need for large-scale image dataset annotation introduced the concept of Automatically Image Annotation (AIA) [7-10]
The current Machine Learning(ML) architecture is centralize in nature for real-time Automatic Image Annotation (AIA) and ML task
Summary
The state-of-art Machine Learning (ML) algorithms are contributing to several sectors of human life through Big Data [01]. Text-based approach manually annotates (through human) the images and inappropriate in the current digitization scenario [3-4]. Content-Based Image Retrieval approach automatically retrieves and index. The need for large-scale image dataset annotation introduced the concept of Automatically Image Annotation (AIA) [7-10]. The AIA technique contains the good characteristics (advantages) from both traditional (text based and CBIR) annotated techniques through the keyword searching based on image content. In AIA, the semantic concept model automatically learns from the large number of visual data. Several studies already discussed AIA advantages over traditional approaches [11-14]. The issue of AIA is it requires a massive (expensive) infrastructure to annotate the large-scale images in real time because these approaches mainly use high computational systems in this study, we proposed a cost-effective AIA approach to generate and annotate the massive amount of dataset
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