Visual search reranking via adaptive particle swarm optimization

Zhang, Lu; Mei, Tao; Liu, Yuan; Tao, Dacheng; Zhou, He-Qin

doi:10.1016/j.patcog.2011.01.016

Cited by 18 publications

(2 citation statements)

References 25 publications

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“…Note that the randomization for particle diversification does not perform a resampling process, as particle filter methods do, 18 since the local best particles provide compact samples for propagation. 26…”

Section: B1 Population Randomizationmentioning

confidence: 99%

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

Luo

Wan

2015

Medical Physics

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Section: B1 Population Randomizationmentioning

confidence: 99%

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

Luo

Wan

2015

Medical Physics

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“…Most reranking methods [8,27,4,30] adopt unimodal features, e.g., textual or visual features for reranking. In this diagram, it is a natural idea to rerank the text based retrieval results by using visual features.…”

Section: Unimodal/multimodal Rerankingmentioning

confidence: 99%

A heterogenous automatic feedback semi-supervised method for image reranking

Wang

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Image reranking, which aims at enhancing the quality of keyword-based image search with the help of image features, recently has become attractive in image search community. A major challenging in this task is that image's visual features do not always well reflect image's semantic meaning. Thus, reranking methods only depending on visual features cannot guarantee to obtain good results. In addition, it is well known that the visual features of an image have strong/weak correlations with its surrounding text. Thus, it is expected that a model considering both visual features and its surrounding text can perform better than those only considering visual features. Motivated by this, in this paper, we propose the HAFSRerank-Heterogenous Automatic Feedback Semi-supervised Reranking method which makes use of both visual and textual features simultaneously during reranking. Specifically, in HAFSRerank, a multigraph is firstly constructed in which each node representing an image includes visual and textual features, and the parallel edges between them are weighted by intra-modal similarity and inter-modal similarity. A heterogenous complete graph is further derived from the multigraph. Then, an automatic feedback graph-based semi-supervised learning method is proposed to propagate the reranking scores on the complete graph, which can make use of the inter-modal similarity to update the weights of heterogenous graph automatically. Finally, the result of the semi-supervised learning is used to rerank the images. The experimental results show that HAFSRerank is superior or highly competitive to some state-of-the-art graph-based reranking methods. Moreover, the proposed reranking algorithm can be well interpreted by Bayesian theory, and does not require complex search models for special queries and any additional input from users.

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Adaptive Tunning of All Parameters in a Multi-Swarm Particle Swarm Optimization Algorithm: An Application to the Probabilistic Traveling Salesman Problem

Marinakis

Marinaki

Migdalas

2015

Springer Proceedings in Mathematics &Amp; Statistics

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One of the main issues in the application of a Particle Swarm Optimization (PSO) algorithm and of every evolutionary optimization algorithm is the finding of suitable parameters for the algorithm. In this paper, we use a parameter free version of a Multi-Swarm PSO algorithm where random values are assigned during the initialization of all parameters (including the number of swarms) of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This idea is used for the solution of the Probabilistic Traveling Salesman Problem (PTSP). The PTSP is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers needs to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. It is compared with other algorithms from the literature, and, mainly with a Multi-Swarm Particle Swarm Optimization with parameters calculated with a classic trial-and-error procedure and they are the same for all instances.

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Visual search reranking via adaptive particle swarm optimization

Cited by 18 publications

References 25 publications

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

A heterogenous automatic feedback semi-supervised method for image reranking

Adaptive Tunning of All Parameters in a Multi-Swarm Particle Swarm Optimization Algorithm: An Application to the Probabilistic Traveling Salesman Problem

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