Scene Categorization with Classified Codebook Model

Xu YANG  De XU  Songhe FENG  Yingjun TANG  Shuoyan LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.6   pp.1349-1352
Publication Date: 2011/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1349
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
codebook model,  codebook generation,  visual words,  classified vector quantization,  scene categorization,  

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This paper presents an efficient yet powerful codebook model, named classified codebook model, to categorize natural scene category. The current codebook model typically resorts to large codebook to obtain higher performance for scene categorization, which severely limits the practical applicability of the model. Our model formulates the codebook model with the theory of vector quantization, and thus uses the famous technique of classified vector quantization for scene-category modeling. The significant feature in our model is that it is beneficial for scene categorization, especially at small codebook size, while saving much computation complexity for quantization. We evaluate the proposed model on a well-known challenging scene dataset: 15 Natural Scenes. The experiments have demonstrated that our model can decrease the computation time for codebook generation. What is more, our model can get better performance for scene categorization, and the gain of performance becomes more pronounced at small codebook size.