We propose a topic Jump search approach considering the topic coverage of the retrieved images and videos. The video ranking based on video likes and keywords, and then it will be comparing with maximum matching keywords.
Storage and retrieval of multimedia has become a requirement for many contemporary information systems. These systems need to provide browsing, querying, navigation, and, sometimes, composition capabilities involving various forms of media. In this survey, we review techniques and systems for image and video retrieval. We first look at visual features for image retrieval such as user’s keywords and spatial relationships.
Temporal aspects of video retrieval and video segmentation are discussed next. We review several systems for image and video retrieval including search, commercial, and World Wide Web-based systems. We conclude with an overview of current challenges and future trends for image and video retrieval.
With the development of social media based on Web 2.0, amounts of images and videos spring up everywhere on the Internet. This phenomenon has brought great challenges to multimedia storage, indexing and retrieval. Generally speaking, tag-based image search is more commonly used in social media than content based image retrieval and content understanding. Thanks to the low relevance and diversity performance of initial retrieval results, the ranking problem in the tag-based image retrieval has gained researchers’ wide attention Nonetheless, the following challenges block the path for the development of re-ranking technologies in the tag-based image retrieval.
A. TAG MISMATCH
Social tagging requires users to label their uploaded images with their own keywords and share with others. Different from ontology based image annotation, there is no predefined ontology or taxonomy in social image tagging. Every user has its own habit to tag images.
Flicker Api is used in the Proposed System and it is used to retrieve the Image from Flicker
By using retrieved image, we can update the description and keywords for both video and image.
User can search by giving the keyword, image and video are retrieved for that keyword.
Most seen videos and Most liked videos are calculated by Page Ranking Algorithm.
System : Pentium IV 2.4 GHz.
Hard Disk : 160 GB.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 1 GB.
Operating system : Windows XP/7/8/10.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4 or Eclipse
Database : MYSQL
X. Li, B. Zhao, and X. Lu, “A general framework for edited video and raw video summarization,” IEEE Trans. Image Process., to be published, doi: 10.1109/TIP.2017.2695887, Apr. 19, 2017.