Random Multimodal Object Classification and Detection Using Deep Learning
| dc.contributor.guide | Lavanya Devi G | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Sunitha Thella | |
| dc.date.accessioned | 2024-10-29T09:10:54Z | |
| dc.date.available | 2024-10-29T09:10:54Z | |
| dc.date.awarded | 2023 | |
| dc.date.completed | 2023 | |
| dc.date.registered | ||
| dc.description.abstract | yearly, the amount of complicated datasets grows, making reliable and precise newlinedata classification necessary. One of the main issues facing the data science newlinecommunity is the categorization and classification of complicated data, including text, newlineimages, audio, and video. A growing corpus of work has been done recently that uses newlinedeep learning architectures and structures for these kinds of issues. newlineInformation Retrieval (IR) is one of the major research areas in the recent newlineyears. There are two types of IR i.e., Content Based and Text Based Retrieval. Text- newlineBased Retrieval is focused on document Retrieval and Content Based Retrieval is newlinefocused on the visual features and also includes audio, video, images, text. Content- newlineBased Information Retrieval includes the CBIR (content-based image retrieval), newlineCBVR (content-based video retrieval) and so on. CBIR system has become a very newlineactive research topic during the last few years, because in contrast to a traditional newlinesystem, the images are retrieved based on the keywords, but the CBIR system newlineretrieves the images based on the visual content. newlineIn the domain of computer vision, the moving object detection is essential and newlinechallenging task as it plays fundamental role in video surveillance, vehicle navigation, newlinegesture recognition, traffic monitoring, categorization, identification, human tracking newlineetc. The traditional moving object detection divides each video frame into foreground newline(moving object) and background (stationary object) regions. newlineMost of the applications use either or all of, text, image, audio or video data newlinetypes. Particularly, social media like Face Book, Instagram, Twitter, LinkedIn etc., are newlineused by many all over the globe to share information. It is necessary to classify and newlinedetect objects in this kind of information for various issues like security, detection of newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | 130 pgs | |
| dc.identifier.uri | http://hdl.handle.net/10603/598562 | |
| dc.language | English | |
| dc.publisher.institution | Department of Computer Science and Systems Engineering | |
| dc.publisher.place | Vishakhapatnam | |
| dc.publisher.university | Andhra University | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Computer Science | |
| dc.subject.keyword | Computer Science Software Engineering | |
| dc.subject.keyword | Engineering and Technology | |
| dc.title | Random Multimodal Object Classification and Detection Using Deep Learning | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
Files
Original bundle
1 - 5 of 13
Loading...
- Name:
- 01_title.pdf
- Size:
- 288.94 KB
- Format:
- Adobe Portable Document Format
- Description:
- Attached File
License bundle
1 - 1 of 1