Automatic image annotation

QT/search is text only research tool. In order to learn how to use it for searching images I collected the following information:

http://research. microsoft. com/users/marycz/semi-auto-annotatoin--full. pdf
Labeling the semantic content of images (or generally, multimedia objects) with a set of keywords is a problem known as image (or multimedia) annotation. Annotation is used primarily for image database management, especially for image retrieval. Annotated images can usually be found using keyword-based search, while non-annotated images can be extremely difficult to find in large databases. Since the use of image-based analysis techniques (what is often called content-based image retrieval) (Flickner et al. , 1995) is still not very accurate or robust, keyword-based image search is preferable and image annotation is therefore unavoidable. In addition, qualitative research by Rodden (1999) suggests that users are likely to find searching for photos based on the text of their annotations as a more useful and likely route in future, computer-aided image databases.

2.http://amazon. ece. utexas. edu/~qasim/research. htm CIRES: Content based Image REtrieval System
CIRES is a robust content-based image retrieval system based upon a combination of higher-level and lower-level vision principles. Higher-level analysis uses perceptual organization, inference and grouping principles to extract semantic information describing the structural content of an image. Lower-level analysis employs a channel energy model to describe image texture, and utilizes color histogram techniques.

3.http://en. wikipedia. org/wiki/CBIR Content-based image retrieval - Wikipedia, the free encyclopedia
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. "Content-based" means that the search makes use of the contents of the images themselves, rather than relying on human-inputted metadata such as captions or keywords. A content-based image retrieval system (CBIRS) is a piece of software that implements CBIR.
There is one problematic issue with the use of the term "Content Based Image Retrieval". The way the term CBIR is generally used, refers only to the structural content of images. This use excludes image retrieval based on textual annotation.
Cortina - Content Based Image Retrieval for 3 Million Images.
Octagon - Free Java based Content-Based Image Retrieval software.

4. http://www. cs. washington. edu/research/imagedatabase Object and Concept Recognition for Content-Based Image Retrieval
These search engines can retrieve images by keywords or by image content such as color, texture, and simple shape properties. Content-based image retrieval is not yet a commercial success, because most real users searching for images want to specify the semantic class of the scene or the object(s) it should contain. The large commercial image providers are still using human indexers to select keywords for their images, even though their databases contain thousands or, in some cases, millions of images. Automatic object recognition is needed, but most successful computer vision object recognition systems can only handle particular objects, such as industrial parts, that can be represented by precise geometric models.

5.http://en. wikipedia. org/wiki/Automatic_image_annotation
Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.
This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations, then techniques were developed using machine translation to try and translate the textual vocabulary with the 'visual vocabulary', or clustered regions known as blobs. Work following these efforts have included classification approaches, relevance models and so on.
The advantages of automatic image annotation versus content-based image retrieval are that queries can be more naturally specified by the user [1]. CBIR generally (at present) requires users to search by image concepts such as color and texture, or finding example queries. Certain image features in example images may override the concept that the user is really focusing on. The traditional methods of image retrieval such as those used by libraries have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly-growing image databases in existence.

6. http://portal. acm. org/citation. cfm?id=860459 Automatic image annotation and retrieval using cross-media . . .
Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labor intensive procedure and hence there has been great interest in coming up with automatic ways to retrieve images based on content. Here, we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, we show that probabilistic models allow us to predict the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models allow us to derive these probabilities in a natural way.

7.http://portal. acm. org/citation. cfm?id=1008992. 1009055 Automatic image annotation by using concept-sensitive salient . . .
Multi-level annotation of images is a promising solution to enable more effective semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components and the relevant semantic concepts. In contrast to the well-known image-based and region-based approaches, we use the salient objects as the dominant image components to achieve automatic image annotation at the content level.

8. http://en. wikipedia. org/wiki/Image_retrieval Image retrieval
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

9.http://citeseer. ist. psu. edu/419422. html
A novel approach to semi-automatically and progressively annotating images with keywords is presented. The progressive annotation process is embedded in the course of integrated keyword-based and content-based image retrieval and user feedback. When the user submits a keyword query and then provides relevance feedback, the search keywords are automatically added to the images that receive positive feedback and can then facilitate keyword-based image retrieval in the future.

10.http://en. wikipedia. org/wiki/Computer_graphics
Blobs: a technique for representing surfaces without specifying a hard boundary representation, usually implemented as a procedural surface like a Van der Waals equipotential (in chemistry).

11.http://runevision. com/3d/blobs
The blob primitive in POV-Ray is a very flexible shape, that can for example be used to create organic-looking shapes. At first it can be a little difficult to understand how blobs work, because the shape of the blob is affected by several variables. The most important variables are the threshold of the blob, the strength of each component, and the radius of each component.