Automated scene understanding via fusion of image and object features

Khosla, Deepak; Uhlenbrock, Ryan; Chen, Yang

doi:10.1109/ths.2017.7943474

Cited by 2 publications

(2 citation statements)

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“…SVM classifiers improve the qualitative accuracy in context understanding between object and natural scene. 90 Closely relevant and important feature extraction is a challenging task in dynamic scene understanding. Type and position of images, scene motion, illumination changes, static and dynamic occlusions, type speed and pose of objects, camera synchronization and handover, event complexity, and handling dynamic scenes are adding more challenges to feature extraction.…”

Section: Image-based Ie-from Visual To Semantic Extractionmentioning

confidence: 99%

“…82 Extracting useful information from these user-generated images is helpful as well as challenging. Data sparsity, 95,96 extracting relevant information from user’s perspective, 76,92,93 semantic understanding, 74,80,89 –91 language understating, object detection, and recognition are major challenges identified in this field. Furthermore, many most critical factors influencing the performance of IE process and techniques from images have been presented in Table 8.…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

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Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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Section: Image-based Ie-from Visual To Semantic Extractionmentioning

confidence: 99%