Image classification for content-based indexing
| Autor: | Anil K. Jain, Aditya Vailaya, Hong-Jiang Zhang, Mário A. T. Figueiredo |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Contextual image classification
Standard test image Computer science business.industry Quantization (signal processing) Feature extraction Search engine indexing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Vector quantization Pattern recognition Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Hierarchical classifier Feature (computer vision) Artificial intelligence business Cluster analysis computer Image retrieval Software |
| Zdroj: | Instituto de Telecomunicações CIÊNCIAVITAE |
| ISSN: | 1057-7149 |
| Popis: | Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier. |
| Databáze: | OpenAIRE |
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