Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval

This paper addresses the problem of semantic-based image retrieval of natural scenes. A typical content-based image retrieval system deals with the query image and images in the dataset as a collection of low-level features and retrieves a ranked list of images based on the similarities between features of the query image and features of images in the image dataset. However, top ranked images in the retrieved list, which have high similarities to the query image, may be different from the query image in terms of the semantic interpretation of the user which is known as the semantic gap. In order to reduce the semantic gap, this paper investigates how natural scene retrieval can be performed using the bag of visual word model and the distribution of local semantic concepts. The paper studies the efficiency of using different approaches for representing the semantic information, depicted in natural scene images, for image retrieval. An extensive experimental work has been conducted to study the efficiency of using semantic information as well as the bag of visual words model for natural and urban scene image retrieval.

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Visual vocabularies generated from image feature vectors are expected to produce visual words that are discriminative to improve the performance of image annotation systems. Most techniques that adopt the BOW model in annotating images declined favorable information that can be mined from image categories to build discriminative visual vocabularies. To this end, this paper introduces a detailed framework for automatically annotating natural scene images with local semantic labels from a predefined vocabulary. The framework is based on a hypothesis that assumes that, in natural scenes, intermediate semantic concepts are correlated with the local keypoints. Based on this hypothesis, image regions can be efficiently represented by BOW model and using a machine learning approach, such as SVM, to label image regions with semantic annotations. Another objective of this paper is to address the implications of generating visual vocabularies from image halves, instead of producing them from the whole image, on the performance of annotating image regions with semantic labels. All BOW-based approaches as well as baseline methods have been extensively evaluated on 6-categories dataset of natural scenes using the SVM and KNN classifiers. The reported results have shown the plausibility of using the BOW model to represent the semantic information of image regions and thus to automatically annotate image regions with labels.