Eigenpaxels and a Neural-Network Approach to Image Classification  

Oleh:

McGuire, P. ; D'Eleuterio, G. M. T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 3 (2001), page 625-635.
Topik: IMAGE; eigenpaxels; neural - network; image classification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A expansion encoding approach to image classification is presented. Localized principal components or “eigenpaxels” are used as a set of basis functions to represent images. That is, principal-component analysis is applied locally rather than on the entire image. The “eigenpaxels” are statistically determined using a database of the images of interest. Classification based on visual similarity is achieved through the use of a single - layer error - correcting neural network. Expansion encoding and the technique of subsampling are key elements in the processing stages of the eigenpaxel algorithm. Tested using a database of frontal face images consisting of 40 individuals, the algorithm exhibits equivalent performance to other comparable but more cumber some methods. In addition, the technique is shown to be robust to various types of image noise.

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