Relevance feature mapping for content-based image retrieval
- Authors: Zhou, Guang , Ting, Kaiming , Liu, Fei , Yin, Yilong
- Date: 2010
- Type: Text , Conference paper
- Relation: 16th ACM SIGKDD Workshop on Multimedia Data Mining p. 1-10
- Full Text: false
- Reviewed:
Relevance feature mapping for content-based multimedia information retrieval
- Authors: Zhou, Guang , Ting, Kaiming , Liu, Fei , Yin, Yilong
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 45, no. 4 (2012), p. 1707-1720
- Full Text: false
- Reviewed:
- Description: This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.
Mass estimation
- Authors: Ting, Kaiming , Zhou, Guang , Liu, Fei , Tan, Swee
- Date: 2013
- Type: Text , Journal article
- Relation: Machine Learning Vol. 90, no. 1 (2013), p. 127-160
- Full Text: false
- Reviewed:
- Description: This paper introduces mass estimation—a base modelling mechanism that can be employed to solve various tasks in machine learning. We present the theoretical basis of mass and efficient methods to estimate mass. We show that mass estimation solves problems effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as well as and often better than eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.