Andere Kunden interessierten sich auch für
![Large-Scale Arabic Text Classification]( "Large-Scale Arabic Text Classification")
Large-Scale Arabic Text Classification38,99 €![Large Scale Cognitive Wireless Networks]( "Large Scale Cognitive Wireless Networks")
Large Scale Cognitive Wireless Networks51,99 €![Image Colorization Using Cross-Scale Patch Matching Technique]( "Image Colorization Using Cross-Scale Patch Matching Technique")
Image Colorization Using Cross-Scale Patch Matching Technique26,99 €![Large-scale Geometric Data Processing and Structured Mesh Generation]( "Large-scale Geometric Data Processing and Structured Mesh Generation")
Large-scale Geometric Data Processing and Structured Mesh Generation29,99 €![Large-scale and high-quality multi-view stereo]( "Large-scale and high-quality multi-view stereo")
Large-scale and high-quality multi-view stereo51,99 €![Dimensionality Reduction For Small, medium And Large Scale Data]( "Dimensionality Reduction For Small, medium And Large Scale Data")
Dimensionality Reduction For Small, medium And Large Scale Data38,99 €![Success Factors for Cloud Computing in Medium Scale Organizations]( "Success Factors for Cloud Computing in Medium Scale Organizations")
Success Factors for Cloud Computing in Medium Scale Organizations26,99 €
Image classification, including object recognition and scene classification, remains to be a major challenge to the computer vision community. As machine can be able to extract information from an image and classify it in order to solve some tasks. Recently SVMs using Spatial Pyramid Matching (SPM) kernel have been highly successful in image classification. Despite its popularity, this technique cannot handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing Vector Quantization to Sparse Coding followed by multi-scale Spatial Max Pooling, and also propose a large scale linear classifier based on Scale Invariant Feature Transform (SIFT) and Sparse Codes. This new adapted algorithm remarkably can handle thousands of training images and classify them into different categories.