NDLI: Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval

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Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval

Content Provider	ACM Digital Library
Author	Xie, Hua Ortega, Antonio Andreu, Victor
Abstract	In this paper, a quantization-based probabilistic feature modeling approach is proposed for relevance feedback in content-based image retrieval. We demonstrate its performance by using the resulting models within a support vector machine (SVM) based technique. Each feature component is quantized and mapped to probabilistic quantities representing the likelihood of the image being relevant (and irrelevant). These probabilistic quantities are then used to derive an information divergence-based kernel function for SVM classification which we introduced in earlier work. We show that the proposed method leads to the optimal maximum likelihood solution as the knowledge of the actual underlying probability model improves (i.e.,as the feature space is partitioned into arbitrarily small "regions "and accurate models are known for all regions). vWe investigate several practical quantization designs for feature modeling specifically in relevance feedback applications,where the scarcity of the data and high dimensionality prevent usage of vector quantization and parametric modeling approaches.Our proposed framework naturally takes into account the statistics of the data that is available during relevance feedback for the purpose of discriminating between relevant and irrelevant images.Experiments with the Corel dataset show that quantizers specifically designed for this application achieve gains over simple uniform quantizers (e.g.,5% to 10% in retrieval accuracy) when combined with our information divergence kernel. This kernel achieves gains (e.g.,17% in retrieval accuracy after first relevance feedback)as compared to the standard radial basis function (RBF) kernel used for SVM-based relevance feedback.
Starting Page	23
Ending Page	32
Page Count	10
File Format	PDF
ISBN	1595934952
DOI	10.1145/1178677.1178684
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Probabilistic modeling Support vector machines Relevance feedback Quantization Kernel
Content Type	Text
Resource Type	Article

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