Local Binary Patterns, M-Band Wavelets and Rotated M-Band Wavelets for Texture Image Retrieval

Abstract

Unlike the standard wavelet decomposition, which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity.

INTRODUCTION

Visual information (images/ video) is one of the most promising sources of multimedia information, as it plays a key role in the communication framework.

The term [CBIR] describes the process of retrieving desired images from a large 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 . The challenge in CBIR is to develop the methods that will increase retrieval accuracy and reduce the retrieval time (computational complexity).

CBIR SYSTEM ARCHITECTURE

A CBIR system consists of two databases namely an Image Database and Image Feature Database.

The image database contains the original images present in the

database.

Similarity between query image and each database image is calculated by finding the distance between the feature vectors.

The Feature Extraction module processes each of the database images to extract a description of the content of the image, represented in the form of a vector called feature vector.

PROBLM FORMLATIONThere are two important tasks in content-based image retrieval. First one is feature extraction, and second one is similarity measurement. Our research is focused on these two important tasks.

This motivates us to explore different similarity measures and different wavelet based features , which will improve retrieval effectiveness both in terms of retrieval accuracyand retrieval time.

A successful CBIR system must be able to deal with textured images in real world. The majority of existing texture feature extraction methods for CBIR assumes that all images are acquired from the same viewpoint. This assumption is not realistic in practical applications.

RELATED WORK

Texture can be defined as, “A region in an image has a constant texture if a set of local statistics or other local properties of the picture are constant, slowly varying, or approximately periodic”.

Texture features currently used in CBIR are mainly derived from Gabor wavelets [62], the conventional discrete wavelet transform (DWT) [53], tree structured wavelet transform [7], and wavelet frame [91].

Texture feature extraction with DWT gives the edge information in the horizontal, vertical and diagonal direction.

Texture representation with the real DWT has two main disadvantages of shift sensitivity and poor directionality (only three directions information).

APPLICATIONS

Art Collectionse.g. Fine Arts Museum of San Francisco

Medical Image Databases :CT, MRI, Ultrasound, The Visible Human

Scientific Databasese.g. Earth Sciences

General Image Collections for Licensing :Corbis, Getty Images

The World Wide Web

Automatic face recognition

FEATURES

Color (histograms, gridded layout, wavelets)

Texture (Laws, Gabor filters, local binary partition)

Shape (first segment the image, then use statistical or structural shape similarity measures)

Objects and their Relationships

METHOD OF FEATURE EXTRATION

To avoid the problem of pixel-by-pixel comparison next abstraction level that is used for representing images is the feature level.

Feature extraction plays an important role in content-based image retrieval to support efficient and fast retrieval of similar images from image

databases.

Significant features should be extracted from image data. Every image is characterized 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 database and stored in image feature database.

Average Retrieval Efficiency: This work is about Content Based Image Retrieval (CBIR), focusing on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness

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 each facility and patient load on each facility

M-Channel waveletsThe main drawbacks of standard wavelets is that they are not suitable for the analysis of high- frequency signals with relatively narrow bandwidth. Also the standard wavelet decomposition gives a logarithmic frequency resolution.

M-Band wavelet on the other hand has two main advantages over the standard wavelet.

M-band wavelet gives better spectral decomposition for texture images than standard wavelet, because M-band wavelet decomposition gives a mixture of a logarithmic and linear frequency resolution.

M-band wavelet decomposition yields a large number of sub bands, which improves the retrieval accuracy.

The limitation of M–band wavelet is that the computational complexityincreases and hence retrieval time increases with number of bands.

M-Channel Filter Bank

The filters hi (n) are analysis filters constituting the analysis filter bank and the 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).

M-band Wavelet

Disadvantage of using standard wavelets is that they are not suitable for the analysis of high-frequency signals with relatively narrow bandwidth.

The M-band orthonormal wavelets give a better energy compaction than two band wavelets by zooming into narrow band high frequency components of a signal

In M-band wavelets there are M-1 wavelets

M-band Wavelet Filters

In the filtering stage we make use of biorthonormal M-band wavelet transform [94] 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[120] used in the experiments.

COSINE MODULATED WAVELETThe 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-phase prototype filter g(n).

The fundamental idea behind cosine-modulated filter banks is the following: In an M-channel filter bank, the analysis and synthesis filters are meant to approximate ideal M th band filters.

In the filtering stage we make use of filter coefficients for M =2 to decompose the texture image in to four channels, corresponding to different direction and resolutions.

After decomposing image with wavelet transform we get horizontal, vertical and diagonal information. Hsin has reported that diagonal filter gives strong response to textures with orientations at or close to ± 45° .

Texture Feature Extraction

Subsequently the decomposition was performed column wise. Thus at the first level of decomposition the original image was decomposed into 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 of 0,45,90,135 degrees.Calculate the energy for all sub bands and from the feature vector.

Experimental Results

The objective of these experiments is to illustrate that the proposed texturefeatures for CBIR using M-band wavelet and cosine modulated wavelet provides equally better retrieval accuracy to that of the Gabor wavelet based method along with much reduced retrieval time. Average retrieval performance with M-Band wavelet (73.65 %) is better than standard wavelet (71.71 %). Average retrieval performance of cosine-modulated wavelet is 74.78% and it is better than standard wavelet. M-Band wavelet and is marginally better than that in case of Gabor wavelet method (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 and cosine-modulated wavelet but five times less as compared to Gabor wavelet.

The retrieval performance of M-Band wavelet is consistently superior to standard wavelet.If the top 116 (6% of the database) retrievals are considered the performance increases up to 91.65%, 94.07 %, 94.77%, and 92.375% using, standard wavelet, M-Band wavelet, cosine-modulated wavelet, and Gabor wavelet respectively.

M-Band Rotated and Directional Wave LetThe analysis was performed up to second level (9×2=18 sub bands) of the wavelet decomposition. The approach is partly supported by physiological studies of the visual cortex as reported by Hubel and Wiesel and Daugman .The energy and standard deviation of decomposed sub bands are computed as follows:

where 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 is the sub band mean value.

1 1

1 M N

k iji j

Energy E WM N

1/2

2

1 1

1( )

M N

k ij iji j

Standard Deviation WM N

ijW

ij

conclusion

The performance of the proposed method is tested by conducting experimentation on Brodatz database. The results after being investigated show a significant improvement in terms of average retrieval rate and average retrieval precision as compared to M_band_DWT, M_band_RWT and other existing transform domain techniques.