Rotation invariant curvelet features for region based image retrieval
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun , Sumana, Ishrat
- Date: 2011
- Type: Text , Journal article
- Relation: International Journal of Computer Vision Vol. 98, no. 2 (2011), p. 187-201
- Full Text: false
- Reviewed:
- Description: There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.
A review on automatic image annotation techniques
- Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 45, no. 1 (2012), p. 346-362
- Full Text: false
- Reviewed:
- Description: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions