Video information retrieval using objects and ostensive relevance feedback

Using Segmented Objects in Ostensive Video Shot Retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partit...

Information Retrieval using Relevance Feedback

Document Image Retrieval Based on Keyword Spotting Using Relevance Feedback

Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

Information Retrieval Advances using Relevance Feedback

Relevance Feedback (RF) is a powerful technique whereby a user can instruct an Information Retrieval (IR) system to find additional relevant documents by providing relevance information on certain documents or query terms. There are several major design decisions that can affect how a RF system can help users. In this paper, I present a brief literature survey covering two emphases of RF: term ...

Automatic relevance feedback for video retrieval

This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. ...