These earlier approaches failed to reduce the semantic gap and were not able to deliver anĪccurate semantic interpretation. ) to high-level ones (semantic objects and concepts). Posed addressing the discovery of features ranging from low-level features (color, histograms, Various approaches for semantic multimedia content analysis have been pro. Tion of objects or events that figure in a content), more particularly, the semantic indexingĮnhancement. Our thesis work deals with the video indexing based on semantic interpretation (an abstrac. This work also indicates additional findings and future research directions. All the information extracted from these papers can be found in a publicly available spreadsheet. As a result, it was found that strategies for cut-based segmentation, color-based indexing, k-means based dimensionality reduction and data clustering have been the most frequent choices in recent papers. By applying a research protocol proposed by us, 153 papers published from 2011 to 2018 were selected. This work designs and conducts a systematic review to find papers able to answer the following research question: “what segmentation, feature extraction, dimensionality reduction and machine learning approaches have been applied for content-based video indexing and retrieval?”. Moreover, relevant topics that can support video retrieval, such as dimensionality reduction, have not been surveyed. However, an extensive and replicable review of the recent literature is missing. Besides performing well, the computational tool is flexible since few changes are required to support other languages.Ĭontent-based video retrieval and indexing have been associated with intelligent methods in many applications such as education, medicine and agriculture. For example, the average word error rate reached down to 0.03 and the mean average precision achieved up to 1.00. The evaluation showed promising results regarding Brazilian Portuguese speech recognition and retrieval performance. An automatic narration scheme was also applied to this subset and the set of 10 videos. As part of this process, one collaborator manually narrated the 50 videos, while four others narrated a subset of 13 videos. We conducted an experimental evaluation of the prototype in sets of 50 and 10 public videos. Afterward, the user can also query by speech or text to find relevant videos previously indexed. Simple text processing techniques are then applied to the obtained transcript before indexing. In particular, the user can narrate each video’s content, generating the utterance that is captured, transformed into text and timestamped by the computational system. This work aims to develop and evaluate a prototype system to index and retrieve videos from speech transcription. However, few initiatives use speech recognition to support both tasks. Video indexing and retrieval have benefited from this resource. Using the voice to interact with systems is attractive in medicine and other areas due to its friendliness and flexibility. The time has come for audiovisual archives to start accommodating content-based video retrieval methods into their daily practice. When combined, we find that content-based video retrieval incorporated into the archive's practice results in significant performance increases for shot retrieval and for retrieving entire television programs. A detailed query-level analysis indicates that individual content-based retrieval methods such as transcript-based retrieval and concept-based retrieval yield approximately equal performance gains. #Searcher2 content stack archiveTo reflect the retrieval practice of both the archive and the video retrieval community as closely as possible, our experiments with three video search engines incorporate archive-created catalog entries as well as state-of-the-art multimedia content analysis results. We utilize logged searches, content purchases, session information, and simulators to create realistic query sets and relevance judgments. To arrive at our main result, we propose an evaluation methodology tailored to the specific needs and circumstances of the audiovisual archive, which are typically missed by existing evaluation initiatives. To the best of our knowledge, this is the first time that the practice of an audiovisual archive has been taken into account for quantitative retrieval evaluation. In this paper, we take into account the information needs and retrieval data already present in the audiovisual archive, and demonstrate that retrieval performance can be significantly improved when content-based methods are applied to search. One such practice is the audiovisual archive, whose users increasingly require fine-grained access to broadcast television content. Content-based video retrieval is maturing to the point where it can be used in real-world retrieval practices.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |