Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automatic discovery of query-class-dependent models for multimodal search
Proceedings of the 13th annual ACM international conference on Multimedia
Exploring temporal consistency for video analysis and retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
The importance of query-concept-mapping for automatic video retrieval
Proceedings of the 15th international conference on Multimedia
Introduction to Information Retrieval
Introduction to Information Retrieval
Reranking Methods for Visual Search
IEEE MultiMedia
CuZero: embracing the frontier of interactive visual search for informed users
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Foundations and Trends in Information Retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multigraph-based query-independent learning for video search
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia answering: enriching text QA with media information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning concept bundles for video search with complex queries
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Semantic-Based Surveillance Video Retrieval
IEEE Transactions on Image Processing
A Survey on Visual Content-Based Video Indexing and Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Utilizing Related Samples to Enhance Interactive Concept-Based Video Search
IEEE Transactions on Multimedia
Hi-index | 0.00 |
We often remember images and videos that we have seen or recorded before but cannot quite recall the exact venues or details of the contents. We typically have vague memories of the contents, which can often be expressed as a textual description and/or rough visual descriptions of the scenes. Using these vague memories, we then want to search for the corresponding videos of interest. We call this “Memory Recall based Video Search” (MRVS). To tackle this problem, we propose a video search system that permits a user to input his/her vague and incomplete query as a combination of text query, a sequence of visual queries, and/or concept queries. Here, a visual query is often in the form of a visual sketch depicting the outline of scenes within the desired video, while each corresponding concept query depicts a list of visual concepts that appears in that scene. As the query specified by users is generally approximate or incomplete, we need to develop techniques to handle this inexact and incomplete specification by also leveraging on user feedback to refine the specification. We utilize several innovative approaches to enhance the automatic search. First, we employ a visual query suggestion model to automatically suggest potential visual features to users as better queries. Second, we utilize a color similarity matrix to help compensate for inexact color specification in visual queries. Third, we leverage on the ordering of visual queries and/or concept queries to rerank the results by using a greedy algorithm. Moreover, as the query is inexact and there is likely to be only one or few possible answers, we incorporate an interactive feedback loop to permit the users to label related samples which are visually similar or semantically close to the relevant sample. Based on the labeled samples, we then propose optimization algorithms to update visual queries and concept weights to refine the search results. We conduct experiments on two large-scale video datasets: TRECVID 2010 and YouTube. The experimental results demonstrate that our proposed system is effective for MRVS tasks.