22-05-2014, 03:54 PM
Sketch 4 Match Content Based Image Retrieval System By Using Sketches
Sketch 4 Match Content.doc (Size: 1.99 MB / Downloads: 17)
INTRODUCTION
Definition of image
Data representing a two-dimensional scene. A digital image is composed of pixels arranged in a rectangular array with a certain height and width. Each pixel may consist of one or more bits of information, representing the brightness of the image at that point and possibly including color information encoded as RGB triples. Picture a visual representation (of an object or scene or person or abstraction) produced on a surface, "they showed us the pictures of their wedding" a movie is a series of images projected so rapidly that the eye integrates them. Effective indexing and retrieving desired image in large image database in the basis of features such as color, text and shape that can be automatically extracted from the images themselves.
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.
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.
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.
JPEG visual descriptors
That shape often carries semantic information follows from the fact that many characteristic objects can be visually recognized solely from their shapes. This distinguishes shape from other elementary visual features such as color, or texture. But the notion of object shape has many meanings. To deal with 3D real-world objects, JPEG standard has a 3D shape descriptor.
JPEG has defined a set of standard descriptors for description and storage of the most commonly used features. This makes the extracted features more accessible. Since the required storage size is much smaller than compressed images files. Moreover, the format of the data is fixed, so the data can be used in any JPEG compatible systems. Thus comparison between algorithms can be done easily if the implementations of the target Algorithms are JPEG compatible.
In JPEG visual standard, some color descriptors are defined, including several histogram based descriptors representing different color features, and a Dominant Color Descriptor (DCD). DCD describes color feature by a set of representative colors with their percentage and each color have at least a certain distance away in CIE color space controlled by a threshold Td. It is very compact since there is no redundant information for non-existed colors, and similar colors are grouped into a palette color.
Relevance Feedback
Although JPEG defined efficient and most commonly used CBIR methods, content based methods still have limitations that they may not be able to find the images that exactly match user’s expectation. One reason is that a precise query cannot be formulated Although DCD can describe color features in a compact and effective way, Drawbacks of its default similarity measure method pull down the performance of DCD.
By just giving an image as query, Interactive searching may be used for improving the retrieval result by refining the query by user’s feedback.