Int J Neural Syst - Structurally enhanced incremental neural learning for image classification with subgraph extraction.

Tópicos

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Resumo

In this paper, a structurally enhanced incremental neural learning technique is proposed to learn a discriminative codebook representation of images for effective image classification applications. In order to accommodate the relationships such as structures and distributions among visual words into the codebook learning process, we develop an online codebook graph learning method based on a novel structurally enhanced incremental learning technique, called as "visualization-induced self-organized incremental neural network (ViSOINN)". The hidden structural information in the images is embedded into the graph representation evolving dynamically with the adaptive and competitive learning mechanism. Afterwards, image features can be coded using a sub-graph extraction process based on the learned codebook graph, and a classifier is subsequently used to complete the image classification task. Compared with other codebook learning algorithms originated from the classical Bag-of-Features (BoF) model, ViSOINN holds the following advantages: (1) it learns codebook efficiently and effectively from a small training set; (2) it models the relationships among visual words in metric scaling fashion, so preserving high discriminative power; (3) it automatically learns the codebook without a fixed pre-defined size; and (4) it enhances and preserves better the structure of the data. These characteristics help to improve image classification performance and make it more suitable for handling large-scale image classification tasks. Experimental results on the widely used Caltech-101 and Caltech-256 benchmark datasets demonstrate that ViSOINN achieves markedly improved performance and reduces the computational cost considerably.

Resumo Limpo

paper structur enhanc increment neural learn techniqu propos learn discrimin codebook represent imag effect imag classif applic order accommod relationship structur distribut among visual word codebook learn process develop onlin codebook graph learn method base novel structur enhanc increment learn techniqu call visualizationinduc selforgan increment neural network visoinn hidden structur inform imag embed graph represent evolv dynam adapt competit learn mechan afterward imag featur can code use subgraph extract process base learn codebook graph classifi subsequ use complet imag classif task compar codebook learn algorithm origin classic bagoffeatur bof model visoinn hold follow advantag learn codebook effici effect small train set model relationship among visual word metric scale fashion preserv high discrimin power automat learn codebook without fix predefin size enhanc preserv better structur data characterist help improv imag classif perform make suitabl handl largescal imag classif task experiment result wide use caltech caltech benchmark dataset demonstr visoinn achiev mark improv perform reduc comput cost consider

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