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We present a novel method for object recognition in noise free and noisy environments, based on modified invariant moments and minimum norm. First, the modified invariant moments of different objects are extracted by using invariant moments. Then the norms of feature vectors are computed by using norm theory of functional analysis. Finally, classification and recognition object are accomplished according...
In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is encoded into a so-called supervector, derived from the universal Gaussian Mixture Models on orderless image patches. Then, a label sparse coding based subspace learning algorithm is derived to effectively harness multi-label...
Image classification based on texture features plays an important role in content-based image retrieval. A novel method for rotation invariant image classification is proposed based on MPEG-7 homogeneous texture descriptor (HTD). To compute HTD, Gabor transform is first performed by filtering image using a bank of orientation and scale selective band-pass filters called Gabor wavelets. For constructing...
With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis...
Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata...
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