13-06-2012, 04:49 PM
image processing
[attachment=24391]
Image processing
The analysis of a picture using techniques that can identify shades, colors and relationships that cannot be perceived by the human eye. Image processing is used to solve identification problems, such as in forensic medicine or in creating weather maps from satellite pictures. It deals with images in bitmapped graphics format that have been scanned in or captured with digital cameras.
Any image improvement, such as refining a picture in a paint program that has been scanned or entered from a video source.
Image Retrieval and Information Retrieval
Since the 1970s Image Retrieval has become a very active research topic, with two major research communities, database management and computer vision. One is text-based and another is visual-based. Text-based image retrieval has become very popular since 1970s, which involves annotating the image with keywords, and use text-based database management systems (DBMS) to retrieve the images. In text-based image retrieval system, keywords of semantic information are attached to the images.
They can be typed manually or by extracting the captions of the images. It is very efficient for simple and small image databases, since the whole database can be described by just few hundreds of keywords. But in the 1990s, several large leaps in development of processor, memory and storage made the size of image databases grow dramatically. As the image database and image size grow, there will be more images having different contents and the images having rich contents cannot be described by only several Semantic keywords. The demand of labor on annotating the images also rises dramatically.
Retrieval image providing effective and efficient tool querying large image database. Information retrieval provides the textual representation of images. It requires the text descriptions to the respective images.
Recent technology development in various fields has made large digital image databases practical. Well organized database and efficient browsing, storing, and retrieval algorithms are very important in such systems. Image retrieval techniques were developed to aid these components.
Image Retrieval was originated from Information Retrieval, which has been very active research topic since 1940s. “We have huge amounts of information to which accurate and speedy access is becoming ever more difficult.” In principle, Information Retrieval is simple. It can be illustrated by a scene of a store of documents and a person (user of the store). He formulates a question to which the answer is a set of documents satisfying his question. He can obtain the set by reading all the documents in the store, retaining the relevant documents and discarding all the others. In this scene, it is a ’perfect’ retrieval. But in practice, we need to model the “read” process in both syntactic and semantic to extract useful information. The target of Information Retrieval is not only “how to extract useful information”, but also “how to measure relevance among documents”. These challenges also exist in Image Retrieval.
Also the keywords are very dependent on the observer’s interest and they are subjective. Captions are not always precisely describing the picture.
Indexing and searching a large image database via keywords are time-consuming and inefficient. Content Based Image Retrieval (CBIR)
researches attempt to automate such complex process of retrieving images that are similar to the reference image or descriptions given.
Content Based Image Retrieval
Content-based image retrieval also known as query by image content and content-based visual information retrieval problem of searching for digital images in large database. Content-based means that the search will analyze the actual contents of image. The term content in this context might refer to colors, shapes, textures or any other information that can be derived from the image itself.
The earliest use of the term Content Based Image Retrieval in the literature seems to be by Kato, was to describe his experiments in automatic retrieval of images from a database by color and shape features. The term has since been widely used to describe the process of retrieving desired images from a large collection on the basis of features (such as color,Texture and shape) that can be automatically extracted from the images themselves. The features used for retrieval can be either primitive or semantic, but the extraction process must be predominantly automatic.
The ideal approach of querying an image database is using content semantics, which applies the human understanding about image. Unfortunately, extracting the semantic information in an image efficiently and accurately is still a question. Even with the most advanced implementation of computer vision, it is still not easy to identify an image of horses on a road. So, using low level features instead of semantics is still a more practical way. Until semantic extraction can be done automatically
and accurately, image retrieval systems cannot be expected to find all correct images. They should select the most similar images to let the user choose the desired images. The number of images of retrieved set can be reduced by applying similarity measure that measures the perceptual similarity.
A typical CBIR system consists of three major components and the variations of them depend on features used.
i. Feature extraction – Analyze raw image data to extract feature specific
Information.
ii. Feature storage – Provide efficient storage for the extracted information, also help to improve searching speed.
iii. Similarity measure – Measure the difference between images for determining the relevance between images.
Content Based Image Retrieval using color
Retrieving image based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an images holding specific values. Current research is attempting to segment color proportion by region and by spatial relationship among several color region.
[attachment=24391]
Image processing
The analysis of a picture using techniques that can identify shades, colors and relationships that cannot be perceived by the human eye. Image processing is used to solve identification problems, such as in forensic medicine or in creating weather maps from satellite pictures. It deals with images in bitmapped graphics format that have been scanned in or captured with digital cameras.
Any image improvement, such as refining a picture in a paint program that has been scanned or entered from a video source.
Image Retrieval and Information Retrieval
Since the 1970s Image Retrieval has become a very active research topic, with two major research communities, database management and computer vision. One is text-based and another is visual-based. Text-based image retrieval has become very popular since 1970s, which involves annotating the image with keywords, and use text-based database management systems (DBMS) to retrieve the images. In text-based image retrieval system, keywords of semantic information are attached to the images.
They can be typed manually or by extracting the captions of the images. It is very efficient for simple and small image databases, since the whole database can be described by just few hundreds of keywords. But in the 1990s, several large leaps in development of processor, memory and storage made the size of image databases grow dramatically. As the image database and image size grow, there will be more images having different contents and the images having rich contents cannot be described by only several Semantic keywords. The demand of labor on annotating the images also rises dramatically.
Retrieval image providing effective and efficient tool querying large image database. Information retrieval provides the textual representation of images. It requires the text descriptions to the respective images.
Recent technology development in various fields has made large digital image databases practical. Well organized database and efficient browsing, storing, and retrieval algorithms are very important in such systems. Image retrieval techniques were developed to aid these components.
Image Retrieval was originated from Information Retrieval, which has been very active research topic since 1940s. “We have huge amounts of information to which accurate and speedy access is becoming ever more difficult.” In principle, Information Retrieval is simple. It can be illustrated by a scene of a store of documents and a person (user of the store). He formulates a question to which the answer is a set of documents satisfying his question. He can obtain the set by reading all the documents in the store, retaining the relevant documents and discarding all the others. In this scene, it is a ’perfect’ retrieval. But in practice, we need to model the “read” process in both syntactic and semantic to extract useful information. The target of Information Retrieval is not only “how to extract useful information”, but also “how to measure relevance among documents”. These challenges also exist in Image Retrieval.
Also the keywords are very dependent on the observer’s interest and they are subjective. Captions are not always precisely describing the picture.
Indexing and searching a large image database via keywords are time-consuming and inefficient. Content Based Image Retrieval (CBIR)
researches attempt to automate such complex process of retrieving images that are similar to the reference image or descriptions given.
Content Based Image Retrieval
Content-based image retrieval also known as query by image content and content-based visual information retrieval problem of searching for digital images in large database. Content-based means that the search will analyze the actual contents of image. The term content in this context might refer to colors, shapes, textures or any other information that can be derived from the image itself.
The earliest use of the term Content Based Image Retrieval in the literature seems to be by Kato, was to describe his experiments in automatic retrieval of images from a database by color and shape features. The term has since been widely used to describe the process of retrieving desired images from a large collection on the basis of features (such as color,Texture and shape) that can be automatically extracted from the images themselves. The features used for retrieval can be either primitive or semantic, but the extraction process must be predominantly automatic.
The ideal approach of querying an image database is using content semantics, which applies the human understanding about image. Unfortunately, extracting the semantic information in an image efficiently and accurately is still a question. Even with the most advanced implementation of computer vision, it is still not easy to identify an image of horses on a road. So, using low level features instead of semantics is still a more practical way. Until semantic extraction can be done automatically
and accurately, image retrieval systems cannot be expected to find all correct images. They should select the most similar images to let the user choose the desired images. The number of images of retrieved set can be reduced by applying similarity measure that measures the perceptual similarity.
A typical CBIR system consists of three major components and the variations of them depend on features used.
i. Feature extraction – Analyze raw image data to extract feature specific
Information.
ii. Feature storage – Provide efficient storage for the extracted information, also help to improve searching speed.
iii. Similarity measure – Measure the difference between images for determining the relevance between images.
Content Based Image Retrieval using color
Retrieving image based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an images holding specific values. Current research is attempting to segment color proportion by region and by spatial relationship among several color region.