Texture Image retrieval
Texture retrieval is the process of retrieving image content based on texture similarity. This may not seem very useful, since we are not using color content. On the other hand, the ability to match on texture similarity can often be useful in distinguishing between areas of images with similar color (such as sky and the sea). Several techniques have been used to measure texture similarity, well known techniques rely on comparing values calculated from query and stored images. From the values, it is possible to calculate measures of image texture such as the degree of contrast,coarseness, dir
ectionality and regularity or periodicity, directionality and randomness.
Different filters such as prewitt, Sobel, Roberts. We use the Sobel filter which is discussed later in the report. Texture queries can be formulated in a similar manner to color queries, by selecting examples of desired textures from a palette, or by supplying an example query image. The system then retrieves images with texture measures most similar in value to the query.
Image Retrieval
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images.
A common saying goes “A picture is better than a thousand words”. Images represented using tags, labels or captioned tend to lose what the actual information the image represents. Images have a large amount of information through human vision and computer vision. Using multiple tags to represent the content of an image simply does not describe an image for efficient retrieval. Content-based image retrieval (CBIR) uses the actual content of the image proving to be more efficient but yet challenging. The most important factor of image retrieval is its accuracy. One problem with using image search results as a training set for a classifier is the high percentage of unrelated images within the results. Estimation has shown a high number of inaccuracies of the result of image in Google image search. Problems with traditional methods of image indexing have led to the rise of interest in techniques for retrieving images on the basis of automatically-derived features such as color, texture and shape.
Most traditional and common methods of image retrieval use methods of adding meta data such as captions, keywords, tags, 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.
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. In other words, an image produces image data in a form of rows and columns. This image data derived from computations can be used to produce vector or quantifiers which would then be a primary key (index) for an image to be retrieved in a large database.
“Content-based” means that the search will analyze the actual contents which in this context; it will be the contents of the image. The term ‘content’ in this context might refer to colors, shapes, textures, or any other visual information that can be derived from the image itself. Without the ability to examine image content, searching for images must rely on meta data or the traditional methods.
Meta data are very hard to generate which proves to be more expensive. A security camera capturing picture could be caption by the time, date, location rather than by the actual contents of the image it represents. Here, CBIR comes into play by deriving image data for analysis and use of different image problem based areas.
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