Conference Paper

Semantic manifold learning for image retrieval.

DOI: 10.1145/1101149.1101193 Conference: Proceedings of the 13th ACM International Conference on Multimedia, Singapore, November 6-11, 2005
Source: DBLP

ABSTRACT Learning the user's semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an augmented relation embedding (ARE) to map the image space into a semantic manifold that faithfully grasps the user's preferences. Besides ARE, we also look into the issues of selecting a good feature set for improving the retrieval performance. With these two aspects of efforts we have established a system that yields far better results than those previously reported. Overall, our approach can be characterized by three key properties: 1) The framework uses one relational graph to describe the similarity relations, and the other two to encode the relevant/irrelevant relations indicated in the feedback. 2) With the relational graphs so defined, learning a semantic manifold can be transformed into solving a constrained optimization problem, and is reduced to the ARE algorithm accounting for both the representation and the classification points of views. 3) An image representation based on augmented features is introduced to couple with the ARE learning. The use of these features is significant in capturing the semantics concerning different scales of image regions. We conclude with experimental results and comparisons to demonstrate the effectiveness of our method.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The “semantic gap” problem is one of the main difficulties in image retrieval tasks. Semi-supervised learning, typically integrated with the relevance feedback techniques, is an effective method to narrow down the semantic gap. However, in semi-supervised learning, the amount of unlabeled data is usually much greater than that of labeled data. Therefore, the performance of a semi-supervised learning algorithm relies heavily on its effectiveness of using the relationships between the labeled and unlabeled data. This paper proposes a novel algorithm to better explore those relationships by augmenting the relational graph representation built on the entire data set, expected to increase the intra-class weights while decreasing the inter-class weights and linking the potential intra-class data. The augmented relational matrix can be directly used in any semi-supervised learning algorithms. The experimental results in a range of feedback-based image retrieval tasks show that the proposed algorithm not only achieves good generality, but also outperforms other algorithms in the same semi-supervised learning framework.
    Applied Intelligence 06/2013; 38(4). · 1.85 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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 average weighted relevance features. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.
    Pattern Recognition. 01/2012; 45:1707-1720.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recently learning to rank has become one of the most common methods to build a ranking model for social image retrieval. However, the results of existing approaches are not so satisfactory for the large gap between low-level visual features and high-level semantic concepts, and these approaches require a significant amount of parameters tuning in the design process to be effective and efficient. In this paper, we propose a novel framework for social image retrieval based on a non-parametric quantum theory, which ranks images by considering their inter-relationship through the quantum estimation without explicit parameter tuning. The basic idea of the proposed framework is inspired by the photon polarization experiment that supports the theory of quantum measurement. Experimental results conducted on the Corel dataset demonstrate the effectiveness and efficiency of the proposed framework.
    National Academy Science Letters 36(3). · 0.07 Impact Factor

Full-text (2 Sources)

Available from
Jun 3, 2014