Codebook Reconstruction with Word Correlation Feedback Mechanism

Bag of feature model has been shown to be one of the most successful methods in generic image categorization problems. However, creating codebook by clustering local feature vectors (e.g. Kmeans) may lose holistic information of images. This paper presents a novel process called Correlation Feedback for codebook construction. It introduces semantic similarities of words by measuring correlation between distributions of them within one image. Further more, we employ label propagation process to spread the affinities among all features. An enhanced codebook is constructed based on fusion of the new similarity matrix with spectral clustering. Experimental results on Caltech101 and the 15 nature scenes datasets shows promising performance of importing the novel similarity to dictionary construction.