Semi-supervised distance metric learning for collaborative image retrieval and clustering
- 27 August 2010
- journal article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Multimedia Computing, Communications, and Applications
- Vol. 6 (3), 1-26
- https://doi.org/10.1145/1823746.1823752
Abstract
Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as “Laplacian Regularized Metric Learning” (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.Keywords
This publication has 23 references indexed in Scilit:
- Collaborative image retrieval via regularized metric learningMultimedia Systems, 2006
- Content-based multimedia information retrievalACM Transactions on Multimedia Computing, Communications, and Applications, 2006
- Integrated probability function and its application to content-based image retrieval by relevance feedbackPattern Recognition, 2003
- Content-based image retrieval at the end of the early yearsIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Data clusteringACM Computing Surveys, 1999
- Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric conesOptimization Methods and Software, 1999
- SHAPE-BASED RETRIEVAL: A CASE STUDY WITH TRADEMARK IMAGE DATABASESPattern Recognition, 1998
- Relevance feedback: a power tool for interactive content-based image retrievalIEEE Transactions on Circuits and Systems for Video Technology, 1998
- Regularization Theory and Neural Networks ArchitecturesNeural Computation, 1995
- Nonlinear programmingACM SIGMAP Bulletin, 1982