IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Clustering and searching WWW images using link and page layout analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Sketch2Photo: internet image montage
ACM SIGGRAPH Asia 2009 papers
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
As the volumes of web images have grown rapidly in the last decade, Content-Based Image Retrieval (CBIR) has attracted substantial interests as an effective tool to manage the images. Most existing CBIR systems focus on the object in the image, while ignoring the conditions (day/night, sunny/rain, etc) and the backgrounds, both of which are very helpful to meet the user's information need. To overcome this shortcoming, in this paper, we present a novel CBIR system depending on a novel query formulation considering three aspects: Object, Background and Condition. Specifically, we design a user-friendly interface to help the user formulate a query. The interface can allow the user to give the percentage, relative position and size of each object in the background. Moreover, a corresponding effective ranking method is proposed to return the desirable search results. Experimental results demonstrate that our proposed system improves the searching performance and the user experience compared with the existing searching systems.