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Unlike the standard wavelet decomposition, which gives a logarithmicfrequency resolution, the M-band decomposition gives a mixture of alogarithmic and linear frequency resolution.Most texture image retrieval systems are still incapable of providing retrievalresult with high retrieval accuracy and less computational complexity.
Visual information (images/ video) is one of the most promising sourcesof multimedia information, as it plays a key role in the communicationframework.The term [CBIR] describes the process of retrieving desired images from alarge collection on the basis of features (such as colour, texture and shape)that can be automatically extracted from the images themselves.The main advantage of CBIR is its ability to support visual queries . Thechallenge in CBIR is to develop the methods that will increase retrievalaccuracy and reduce the retrieval time (computational complexity).
A CBIR system consists of two databases namely an Image Database andImage Feature Database.The image database contains the original images present in the database.Similarity between query image and each database image is calculated byfinding the distance between the feature vectors.The Feature Extraction module processes each of the database images toextract a description of the content of the image, represented in the form of avector called feature vector.
There are two important tasks in content-based image retrieval. First oneis feature extraction, and second one is similarity measurement. Ourresearch is focused on these two important tasks.This motivates us to explore different similarity measures and differentwavelet based features , which will improve retrieval effectiveness both interms of retrieval accuracyand retrieval time.A successful CBIR system must be able to deal with textured images in realworld. The majority of existing texture feature extraction methods for CBIRassumes that all images are acquired from the same viewpoint. Thisassumption is not realistic in practical applications.
Texture can be defined as, “A region in an image has a constant texture if a setof local statistics or other local properties of the picture are constant, slowlyvarying, or approximately periodic”.Texture features currently used in CBIR are mainly derived from Gaborwavelets , the conventional discrete wavelet transform (DWT) , treestructured wavelet transform , and wavelet frame .Texture feature extraction with DWT gives the edge information in thehorizontal, vertical and diagonal direction.Texture representation with the real DWT has two main disadvantages ofshift sensitivity and poor directionality (only three directions information).
Art Collectionse.g. Fine Arts Museum of San Francisco Medical Image Databases :CT, MRI, Ultrasound, The Visible HumanScientific Databasese.g. Earth Sciences General Image Collections for Licensing :Corbis, Getty ImagesThe World Wide WebAutomatic face recognition
Color (histograms, gridded layout, wavelets) Texture (Laws, Gabor filters, local binary partition) Shape (first segment the image, then use statistical or structural shapesimilarity measures) Objects and their Relationships
To avoid the problem of pixel-by-pixel comparison next abstraction level that isused for representing images is the feature level.Feature extraction plays an important role in content-based image retrieval tosupport efficient and fast retrieval of similar images from image databases.Significant features should be extracted from image data. Every image ischaracterized by a set of features such as texture, color, shape, spatial location,image semantic features etc.These features are extracted at the time of injecting new image in image databaseand stored in image feature database.
Average Retrieval Time:A new optimization criterion for locating emergency medical care facilities,level-load retrieval time, is described and applied to Los Angeles County.The new criterion combines retrieval times from demand points served by eachfacility and patient load on each facility
The main drawbacks of standard wavelets is that they are not suitable for theanalysis of high- frequency signals with relatively narrow bandwidth. Also thestandard wavelet decomposition gives a logarithmic frequency resolution. M-Band wavelet on the other hand has two main advantages over thestandard wavelet.M-band wavelet gives better spectral decomposition for texture images thanstandard wavelet, because M-band wavelet decomposition gives a mixture of alogarithmic and linear frequency resolution.M-band wavelet decomposition yields a large number of sub bands, whichimproves the retrieval accuracy.The limitation of M–band wavelet is that the computational complexityincreases and hence retrieval time increases with number of bands.
The filters hi (n) are analysis filters constituting the analysis filter bank andthe filters gi (n) are the synthesis filters constituting the synthesis filter bank.Perfect reconstruction of the signal is an important requirement of M-Channel filter bank. Filter bank is said to be perfect reconstruction if y(n) = x(n).
Disadvantage of using standard wavelets is that they are not suitable for theanalysis of high-frequency signals with relatively narrow bandwidth.The M-band orthonormal wavelets give a better energy compaction than twoband wavelets by zooming into narrow band high frequency components of asignalIn M-band wavelets there are M-1 wavelets
In the filtering stage we make use of biorthonormal M-band wavelettransform  to decompose the texture image into M ×M -channels,corresponding to different direction and resolutions.At each level with M=3, the image is decomposed in toM ×M (=9) channels. Table 3.1 shows the 3-band wavelet filter coefficients used in the experiments.
The cosine –modulated FIR filter banks are the special class of unitary filter banks,where the analysis filters hi (n) are all cosine-modulates of a low pass linear-phaseprototype filter g(n).The fundamental idea behind cosine-modulated filter banks is the following: Inan M-channel filter bank, the analysis and synthesis filters are meant to approximateideal M th band filters.In the filtering stage we make use of filter coefficients for M =2 to decompose thetexture image in to four channels, corresponding to different direction andresolutions.After decomposing image with wavelet transform we get horizontal, vertical anddiagonal information. Hsin has reported that diagonal filter gives strong responseto textures with orientations at or close to ± 45° .
Subsequently the decomposition was performed column wise.Thus at the first level of decomposition the original image was decomposedinto M 2 = 9 sub-images. This would correspond to the decomposition of upper left-hand corner sub-band of the frequency plane called a complete decomposition. In general we obtain M 2n sub-bands at the nth level of decomposition.Rotate these sub bands by +45 deg, we will get the information in directions of0,45,90,135 degrees.Calculate the energy for all sub bands and from the feature vector.
The objective of these experiments is to illustrate that the proposed texturefeatures for CBIR using M-band wavelet and cosine modulated wavelet providesequally better retrieval accuracy to that of the Gabor wavelet based methodalong with much reduced retrieval time.Average retrieval performance with M-Band wavelet (73.65 %) is better thanstandard wavelet (71.71 %).Average retrieval performance of cosine-modulated wavelet is 74.78% and it isbetter than standard wavelet. M-Band wavelet and is marginally better than that in case of Gabor waveletmethod (74.32%) proposed by Ma and Manjunath.
In terms of feature extraction time for query image, the Gabor waveletis most expensive.Computational complexity of M-Band wavelet is more as compared to standard wavelet andcosine-modulated wavelet but five times less as compared to Gabor wavelet.
The retrieval performance of M-Band wavelet is consistently superior tostandard wavelet.If the top 116 (6% of the database) retrievals are considered the performanceincreases up to 91.65%, 94.07 %, 94.77%, and 92.375% using, standard wavelet,M-Band wavelet, cosine-modulated wavelet, and Gabor wavelet respectively.
The analysis was performed up to second level (9×2=18 sub bands) of thewavelet decomposition.The approach is partly supported by physiological studies of the visual cortexas reported by Hubel and Wiesel and Daugman .The energy and standard deviation of decomposed sub bands are computedas follows: M N 1 Energy Ek W ij M N i 1 j 1 1/ 2 M N 1 2 Standard D eviation k (W ij ij ) M N i 1 j 1where W ij is the wavelet-decomposed sub band, M×N is the size of wavelet-decomposed sub band, k is the number of sub bands (k=18 for two levels), and ijis the sub band mean value.
The performance of the proposed method is tested by conductingexperimentation on Brodatz database.The results after being investigated show a significant improvement in termsof average retrieval rate and average retrieval precision as compared toM_band_DWT, M_band_RWT and other existing transform domaintechniques.