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Image Retrieval. Chapter 5. Image Retrieval. Information retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web. [1]
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Image Retrieval Chapter 5
Image Retrieval • Information retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web. [1] • Image retrieval or image search 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.
Content-based image retrieval (CBIR) • Content-based means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the 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. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.
Image Processing for Retrieval • Representing the Images • image Features: Information about color or texture or shape which are extracted from an image. • Color : red • Texture: sandy • Shape: square • Image segmentation Do we consider the whole image or just part ? • Whole image - global features • Parts of image - local features
Image Processing for Retrieval Global features: • Averages across whole image • Tends to loose distinction between foreground and background. • Poorly reflects human understanding of images. • Computationally simple. Local Features: • Two sorts: • Tile Based • Region based
Image Segmentation Tiles Regions
Image Segmentation • Tiling • Break image down into simple geometric shapes • Similar Problems to Global • Plus dangers of breaking up significant objects • Computational Simple • Some Schemes seem to work well in practice • Regioning • Break Image down into visually coherent areas • Can identify meaningful areas and objects • Computationally intensive • Unreliable
image Features • Color: • Produce a color signature for region/whole image • Typically done using color histograms • Color Histograms: • Identify a number of buckets in which to sort the available colours (e.g. red green and blue, or up to ten or so colours). • Allocate each pixel in an image to a bucket and count the number of pixels in each bucket. • Use the figure produced (bucket id plus count, normalised for image size and resolution) as the index key (signature) for each image.
Texture • Produce a mathematical characteristics of a repeating pattern in the image: • Smooth • Sandy • Grainy • Stripy Reduces an area/region to a small set of numbers which can be used a signature for that region.
Shape • It depends on the principle of object recognition • Difficult and Less Commonly used. All objects have closed boundaries Shape can be also found In segmentation Find the duck shapes
shape • Match the duck:
Indexing and Retrieving Images • An Index: A data structure which stores data in a suitably abstracted and compressed form in order to facilitate rapid processing by an application.
Distortion Filtering • Distortion means altering the image in a way that might not be suitable or desired. • Distortion happens by adding some of the noise models to the image in order to estimate the degradation function applied to the image and restore the image to its origin.
Noise Models • The principal sources of noise in digital images arise during image acquisition and/or transmission. • The performance of imaging sensors is affected by a variety of factors, such as: • Environmental conditions during image acquisition • Quality of the sensing elements themselves. • Some Important Noise Probability Density Functions • Gaussian noise • Rayleigh noise • Erlang(gamma) noise • Exponential noise • Uniform noise • Impulse(salt-and-pepper) noise
Restoring the image after applying (Noise Models (spatial Filtering • When the only degradation present in an image is noise. • Spatial filtering is the method of choice in situations when only additive random noise is present. Spatial filtering was discussed in details in Ch3. • Mean Filters: • Arithmetic mean filter This is the simplest of the mean filters, the arithmetic mean filter computes the average value of the corrupted image g(x,y). This operation can be implemented using a spatial filter of size m x n in which all coefficients have value 1/mn. A mean filter smoothes local variations in an image, and noise is reduced as a result of blurring. • Geometric mean filter: Here each restored pixel is given by the product of the pixels in the subimage window, raised to the power 1/mn. A geometric mean filter achieves smoothing comparable to the arithmetic mean filter, but it tends to lose less image detail in the process.
Mean Filters: • Harmonic mean filter The harmonic mean filter works well for salt noise, but fails for pepper noise. It does well also with other types of noise like Gaussian moise. • Contraharmonic mean filter: It has order of the filter Q.This filter is well suited for reducing or eleminating the effects of salt-and-pepper noise. For positive values of Q , the filter eleminates pepper noise. For negative valuse of Q it eleminates salt noise. It cannot do both simultaneously. Note that contraharmonic filter reduces to the arithmetic filter mean filter if Q=0, and to the harmonic mean filter if Q=-1.
Order-Statistic Filters • Order-statistic filters are spatial filters whose response is based on ordering(ranking) the values of the pixels contained in the image area encompassed by the filter. • Median filter The best known order-statistic filter is the median filter, which replaces the value of a pixel by the median of the intensity levels in the neighborhood of the pixel. They provide excellent noise-reduction capabilities, with less blurring than linear smoothing filters if similar size. • Max and min filters • The median filter represents the 50th percentile of a ranked set of numbers. Using the 100th percentile results in so called max filter. • This filter is useful for finding the brightest points in an image. Also, because pepper noise had very low values, it is reduced by this filter as a result of the max selection process in the subimage area. • The 0th percentile filter is the min filter. This filter is useful for finding the darkest points in an image. Also, it reduces salt noise as a result of the min operation.
Order-Statistic Filters • Midpoint filter the midpoint filter simply computes the midpoint between the maximum and minimum values in the area encompassed by the filter: (x,y) = ½(max + min) note that this filter combines order statistics and averaging. It works best for randomly distributed noise, like Gaussian or uniform noise.
