Semantic Bag-of-Words Models for Visual Concept Detection and Annotation

This paper presents a novel method for building textual feature defined on semantic distance and describes multi-model approach for Visual Concept Detection and Annotation(VCDA). Nowadays, the tags associated with images have been popularly used in the VCDA task, because they contain valuable information about image content that can hardly be described by low-level visual features. Traditionally the term frequencies model is used to capture this useful text information. However, the shortcoming in the term frequencies model lies that the valuable semantic information can not be captured. To solve this problem, we propose the semantic bag-of-words(BoW) model which use WordNet-based distance to construct the codebook and assign the tags. The advantages of this approach are two-fold: (1) It can capture tags semantic information that is hardly described by the term frequencies model. (2) It solves the high dimensionality issue of the codebook vocabulary construction, reducing the size of the tags representation. Furthermore, we employ a strong Multiple Kernel Learning (MKL) classifier to fuse the visual model and the text model. The experimental results on the Image CLEF 2011 show that our approach effectively improves the recognition accuracy.