Video Classification Using Low-Level Components and Computable Features Assessment
نویسندگان
چکیده
منابع مشابه
Semantic Film Preview Classification Using Low-Level Computable Features
This paper presents a framework for the classification of feature films into genres, based on computable visual cues. The authors view the work as a step towards high-level semantic film interpretation, currently using low-level video features and knowledge of ubiquitous cinematic practices. Our current domain of study is the film preview (the commercial advertisements primarily created to attr...
متن کاملSegmenting and Recognizing Human Action using Low-level Video Features
Dividing observed human behavior into individual, meaningful actions is a critical task for both human learners and computer vision systems. An important question is how much action structure and segmentation information is available in the observed surface level motion and image changes, without any knowledge of human pose or behavior. Here we present a novel approach to jointly segmenting and...
متن کاملAggregating Frame-level Features for Large-Scale Video Classification
This paper introduces the system we developed for the Google Cloud & YouTube-8M Video Understanding Challenge, which can be considered as a multi-label classification problem defined on top of the large scale YouTube-8M Dataset [1]. We employ a large set of techniques to aggregate the provided frame-level feature representations and generate video-level predictions, including several variants o...
متن کاملHigh Quality Video Assessment Using Salient Features
An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for diffe...
متن کاملVideo captioning with recurrent networks based on frame- and video-level features and visual content classification
In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks, which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning system proposed in [22] and implemented in [8] and extends this framework to videos utilizing both static image features and v...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of New Technology and Research
سال: 2018
ISSN: 2454-4116
DOI: 10.31871/ijntr.4.8.63