Automatic image annotation thesis

Shangwen Li and C.

Automatic image annotation thesis

This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract With the progress of network technology, there are more and more digital images of the internet.

But most images are not semantically marked, which makes it difficult to retrieve and use. In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization PSO and support vector clustering SVC. The algorithm includes two stages: In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm.

Introduction With the popularization of digital cameras and other digital devices, the number of images of the network has increased exponentially [ 1 ], and image retrieval technology has become a hot research topic.

According to the different retrieval methods, image retrieval technology can be divided into two categories: The advantage of TBIR is convenient, and users can query and get the relevant results by searching the relevant keywords.

However it requires manual annotation of images, the workload is very massive. CBIR searches similar images based on the visual characteristics of images.

Although there are many works about CBIR [ 5 — 10 ], the semantic gap still exists because the images are annotated based on their low-level features such as color and texture. Many studies combine semantic information to improve content-based image retrieval techniques, and semantic information is usually composed of textual keywords that describe the semantic attributes of images.

Because manually annotating semantic information is a very time-consuming and laborious work, automatic image annotation has become an increasingly crucial problem in image retrieval [ 11 — 19 ].

Because of the semantic gap, a gap between the low-level visual features such as colors and textures and the high-level concept which are usually used by the user in searching process, the accuracy of CBIR is not adequate. SVC is used to model the data uniformly, so as to describe the image cluster containing the same semantics with the unified model.

The problem of the semantic gap is further solved by different models describing the image clusters with different semantics. Experiment results show the effectiveness of the algorithm.

Related Work This work is related to use the support vector machine in automatic annotation images and some related works are reviewed for this section. All the images are handled through taking for every pixel a constant number of partially overlapping image subdivisions which include it.

Then, each of them is classified by a multi-class SVM. Each pixel is assigned to one of the categories using the results. A confidence-based dynamic ensemble which was based on SVM was proposed to multiclass image annotation [ 21 ].

For false positive and false negative examples, they introduced asymmetrical loss functions to extend the conventional SVM to MIL setting.


The MIL-based bag features can be got through employing MIL on the image blocks, where a faster searching algorithm and enhanced diversity density algorithm are used to improve the accuracy and the efficiency. The results are further input to a set of SVM to find the optimum hyperplanes for annotating training images.

Homogeneously, color features and global texture are input another set of SVM to categorize training images.Zhigang Ma, Yi Yang, Alexander G. Hauptmann and Nicu Sebe.

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ACM International Conference on Multimedia Retrieval, Exploiting the Entire Feature Space with Sparsity for Automatic Image Annotation. The nature of automatic image annotation (AIA) is a process of learning, that is, associating images with semantic keywords according to their visual contents [1].

(English) Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits Student thesis Abstract [en] Image annotations, often in the form of tags, are very useful when indexing large image collections.

feature selections have ever been applied in automatic image annotation and retrieval. To determine the correlation between keywords and blob-tokens, we need to apply statistical models to estimate the correspondence between each pair of keyword and.

Automatic Image Annotation of News Images with Large Vocabularies and Low Quality Training Data J.

Automatic image annotation thesis

Jeon and R. Manmatha Center for Intelligent Information Retrieval Computer Science Department University of Massachusetts Amherst, MA Automatic Bibliography Maker Build a bibliography or works cited page the easy way Annotation: Extra notes about a source you are citing.

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