Image annotation with semi-supervised clustering

Download

index.pdf

Date

2009

Author

Sayar, Ahmet

Metadata

Show full item record

Item Usage Stats

241
views

135
downloads

Image annotation is defined as generating a set of textual words for a given image, learning from the available training data consisting of visual image content and annotation words. Methods developed for image annotation usually make use of region clustering algorithms to quantize the visual information. Visual codebooks are generated from the region clusters of low level visual features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this thesis, we propose a new image annotation technique, which improves the representation and quantization of the visual information by employing the available but unused information, called side information, which is hidden in the system. This side information is used to semi-supervise the clustering process which creates the visterms. The selection of side information depends on the visual image content, the annotation words and the relationship between them. Although there may be many different ways of defining and selecting side information, in this thesis, three types of side information are proposed. The first one is the hidden topic probability information obtained automatically from the text document associated with the image. The second one is the orientation and the third one is the color information around interest points that correspond to critical locations in the image. The side information provides a set of constraints in a semi-supervised K-means region clustering algorithm. Consequently, in generation of the visual terms from the regions, not only low level features are clustered, but also side information is used to complement the visual information, called visterms. This complementary information is expected to close the semantic gap between the low level features extracted from each region and the high level textual information. Therefore, a better match between visual codebook and the annotation words is obtained. Moreover, a speedup is obtained in the modified K-means algorithm because of the constraints brought by the side information. The proposed algorithm is implemented in a high performance parallel computation environment.

Subject Keywords

Electronic computers., Image processing.

URI

http://etd.lib.metu.edu.tr/upload/3/12611251/index.pdf
https://hdl.handle.net/11511/19128

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Novel refinement method for automatic image annotation systems Demircioğlu, Erşan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2011) Image annotation could be defined as the process of assigning a set of content related words to the image. An automatic image annotation system constructs the relationship between words and low level visual descriptors, which are extracted from images and by using these relationships annotates a newly seen image. The high demand on image annotation requirement increases the need to automatic image annotation systems. However, performances of current annotation methods are far from practical usage. The most ...
Image Annotation With Semi-Supervised Clustering Sayar, Ahmet; Yarman Vural, Fatoş Tunay (2009-09-16) Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using three types of side information. The first one is the topic probability information obtained from the text document associated with the image. The second is the orientation an...
HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2009-06-25) Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hierar...
Automatic image annotation by ensemble of visual descriptors Akbaş, Emre; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2006) Automatic image annotation is the process of automatically producing words to de- scribe the content for a given image. It provides us with a natural means of semantic indexing for content based image retrieval. In this thesis, two novel automatic image annotation systems targeting dierent types of annotated data are proposed. The rst system, called Supervised Ensemble of Visual Descriptors (SEVD), is trained on a set of annotated images with predened class labels. Then, the system auto- matically annotates...
Recursive shortest spaning tree algorithms for image segmentation Bayramoğlu, Neslihan Yalçın; Bazlamaçcı, Cüneyt Fehmi; Department of Electrical and Electronics Engineering (2005) Image segmentation has an important role in image processing because it is a tool to obtain higher level object descriptions for further processing. In some applications such as large image databases or video image sequence segmentations, the speed of the segmentation algorithm may become a drawback of the application. This thesis work is a study to improve the run-time performance of a well-known segmentation algorithm, namely the Recursive Shortest Spanning Tree (RSST). Both the original and the fast RSST...

Citation Formats

A. Sayar, “Image annotation with semi-supervised clustering,” Ph.D. - Doctoral Program, Middle East Technical University, 2009.