Search

A single feature is hard to describe the content of images from an overall perspective, which limits the retrieval performances of single-feature-based methods in image retrieval tasks. To fully describe the properties of images and improve the retrieval performances, multifeature fusion ranking-based methods are proposed. However, the effectiveness of multifeature fusion in image retrieval has not been theoretically explained. This article gives a theoretical proof to illustrate the role of independent features in improving the retrieval results. Based on the theoretical proof, the original ranking list generated with a single feature greatly influences the performances of multifeature fusion ranking. Inspired by the principle of three degrees of influence in social networks, this article proposes a reranking method named $k$ -nearest neighbors’ neighbors’ neighbors’ graph (N3G) to improve the original ranking list by a single feature. Furthermore, a multigraph fusion ranking (MFR) method motivated by the group relation theory in social networks for multifeature ranking is also proposed, which considers the correlations of all images in multiple neighborhood graphs. Evaluation experiments conducted on several representative data sets (e.g., UK-bench, Holiday, Corel-10K, and Cifar-10) validate that N3G and MFR outperform the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video-based person re-identification (re-id) matches two tracks of persons from different cameras. Features are extracted from the images of a sequence and then aggregated as a track feature. Compared to existing works that aggregate frame features by simply averaging them or using temporal models such as recurrent neural networks, we propose an intelligent feature aggregate method based on reinforcement learning. Specifically, we train an agent to determine which frames in the sequence should be abandoned in the aggregation, which can be treated as a decision making process. By this way, the proposed method avoids introducing noisy information of the sequence and retains these valuable frames when generating a track feature. On benchmark data sets, experimental results show that our method can boost the re-id accuracy obviously based on the state-of-the-art models. [ABSTRACT FROM AUTHOR]

We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs. WCS matching, as a natural generalization of equal-sized graph matching and subgraph matching, has found wide applications in many computer vision and machine learning tasks. In this brief, WCS matching is first formulated as a combinatorial optimization problem over the set of partial permutation matrices. Then, it is approximately solved by a recently proposed combinatorial optimization framework—graduated nonconvexity and concavity procedure. Experimental comparisons on both synthetic graphs and real-world images validate its robustness against noise level, problem size, outlier number, and edge density. [ABSTRACT FROM AUTHOR]

In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency. [ABSTRACT FROM AUTHOR]