Database retrieval for similar images using ICA and PCA bases

Katsumata, N; Matsuyama, Yasuo

doi:10.1016/j.engappai.2005.01.002

Cited by 36 publications

(11 citation statements)

References 10 publications

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“…The speedup is drastic if the sources are superGaussian, which is the case when the source data are natural images. Since ICA bases have the roles of expressing texture information for data compression and measuring the similarity between different images, they can be used in this system for the similar-image retrieval [7]. In such a case, the speedup of the RapidICA is quite good.…”

Section: Discussionmentioning

confidence: 90%

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Rapid Algorithm for Independent Component Analysis

Yokote¹,

Matsuyama²

2012

JSIP

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A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. This can be viewed as the addition of a variable-size momentum term. The use of past information comes from the idea of surrogate optimization. There is little additional cost for either software design or runtime execution when past information is included. The speed of the algorithm is evaluated on both simulated and real-world data. The real-world data includes color images and electroencephalograms (EEGs), which are an important source of data on human-computer interactions. From these experiments, it is found that the method we present here, the RapidICA, performs quickly, especially for the demixing of super-Gaussian signals.

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Section: Discussionmentioning

confidence: 90%

“…Among the various ICA methods, the FastICA, which is a fixed-point algorithm [2], is the most popular one because it usually out-performs the fastest version of the gradient-style algorithms [3][4][5]. However, the need for ever faster ICAs has arisen ubiquitously [6,7].…”

Section: Introductionmentioning

confidence: 99%

Rapid Algorithm for Independent Component Analysis

Yokote¹,

Matsuyama²

2012

JSIP

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“…However, ICA uses a higher-order statistical method while PCA uses a second-order method. Thus, more useful information may be extracted from the data in ICA than PCA (Back & Weigend, 1997;Cao, Chua, Chong, Lee, & Gu, 2003;Du, Hu, & Shyu, 2004;Fragos, Stergioulas, & Xydeas, 2003;Katsumata & Matsuyama, 2005;Kermit & Tomic, 2003).…”

Section: Introductionmentioning

confidence: 99%

Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing

Ahn

Choi

Han

2007

Expert Systems with Applications

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“…It was developed in the 1990s [57]- [58] since then, this analysis method has served as a tool in separating independent sources variables from a linear mixture which is widely used in many fields of applied science and engineering such as biomedical [59], image processing [60] and face recognition [61]. Moreover, ICA had been used for process separation of gas-liquid through electrical resistance tomography done by Xu et al [62].…”

Section: Turbidity Measurement Of Liquidmentioning

confidence: 99%