Evaluation on Colour Image Database

The discrete wavelet frames texture method described in the previous section will now be evaluated on the colour image database. To achieve this, the collection from the Vision Texture (VisTex) (123) database will be used. A total of 2672 128×128 database images are produced from the original 167 512×512 images. Each image is converted to a grey level image using the luminance function:

Luminence, L= 0.299R+ 0.587G+ 0.114B (4.37) where R, G and B is the red, green and blue components of the colour spaces respectively. If the Vision Texture database is visually inspected, there are parts of the original images that are almost identical. Compared to the Brodatz texture database, it is more rigorous in distinguishing textures. With human visual discrimination, using the VisTex sub-images, it is impossible to decide which original texture to associate some of the sub- images with. This can severely distort thetrueaccuracy of the texture algorithm. There are also more collections on highly inhomogeneous textures existing in the database, such as the Buildings and Paintings, compared to Brodatz textures. Figure 4.21 illustrates some of the patterns in VisTex. There is hardly any difference between Tile.0000 and Tile.0001, whereas Buildings.0005 and Painting.1.0000 are significantly uneven over the whole image. In appendix C, a list of which VisTex textures belong to the same class is presented, as well as which textures will not be included in the performance evaluation. To obtain the true accuracy of the retrieval, the precision and recall approaches employed in the previous section need to be modified. This is because since some sub-images such as the ones from Tile.0000 and Tile.0001 are grouped together as one class, the size of each classes will no longer be the same. Some will have 16, and some will have 32. The most extreme case is within the Terrain classes where all 11 parent images are very similar, hence this class will consists of 11×16 = 176 database images. It is therefore unfair to measure the recognition rate at R = 15 for all classes. In order to observe the retrieval performance of the VisTex database, the following approach is employed. For a texture class of sizeN, we observe how many of the database images within this

Tile.0000 Tile.0001 Buildings.0005 Paintings.1.0000 Figure 4.21: Examples of very similar textures and highly inhomogeneous textures of

the VisTex database

class is retrieved within the top N −1 retrieved images when one of them is used as query. For example, for a class of size 16, the recognition rate is recorded for the top 15, while for class of size 32 and 176, the recognition rate is recorded for the top 31 and 175 respectively.

The recognition rate for each VisTex class is shown in Appendix D. It was observed that the average recognition rate for the VisTex database is 68.58%. This is quite a high retrieval rate considering the level of confusion the Vistex database brought with them. The performance of standard deviation energy and the zero-crossings individually was also observed, and is recorded to be 56.55% and 53.52% respectively. This further confirms the superiority of the combined features over the individual features for VisTex database. To investigate the effectiveness of the luminence function in converting colour images into grey scale images, a selection of several colour to grey scale conversion for- mulae were also evaluated. The selected colour to grey scale conversions are summarized below: • CL₁: I = 0.333R+ 0.333G+ 0.333B • CL₂: I = 0.405R+ 0.116G+ 0.133B • CL3: I = 0.145R+ 0.827G+ 0.627B • CL4: I = 0.596R−0.274G−0.322B • CL5: I = 0.211R−0.253G+ 0.312B • CL6: I = 0.500R−0.500B • CL7: I =−0.5R+G−0.5B • CL8: I =R • CL9: I =G • CL₁₀: I =B

Table 4.14 shows the average recognition rate for the different colour to grey scale conversion. From the table, except for a few conversion methods, all the colour to grey scale conversion approaches give quite a similar performance. We can conclude that the choice of colour to grey-scale conversion is not very critical in texture retrieval using discrete wavelet frames, and the two most commonly used approaches, the luminence and the average,CL1gives a comparable performance. Figure 4.22 shows some retrieval

examples of the VisTex database experiments.

Conversion Type Average Recognition Rate Luminence,CL0 68.58 CL1 69.02 CL2 68.97 CL3 68.22 CL4 63.77 CL5 69.12 CL6 64.35 CL7 65.82 CL8 68.18 CL9 67.46 CL₁₀ 67.00

Table 4.14: Average recognition rate for different colour to grey scale conversion

4.4

Chapter Summary

This chapter started with a brief description of some popular texture feature extraction methods. An experiment was then conducted to evaluate the performance of wavelet- based texture features, which are the PWT, TWT and the DWF. Several other tech- niques (Gabor transform, DCT, Law’s texture feature, co-occurrence matrix and MR- SAR) were also investigated for comparison with the wavelet-based method. From the experiment, it was found that the wavelet-based method performs quite comparably with the other method, with only the Gabor transform and the MRSAR techniques showing better retrieval performance. However the wavelet-based techniques have a very impor- tant advantage of very fast computation, hence making it the optimal choice for texture retrieval. Among the three wavelet-based techniques, the DWF was chosen for further experimentation because it is slightly better than the other two, as well as the fact that it is translationally invariant.

The next experiments were focused on the discrete wavelet frames where several impor- tant parameters were investigated for its influence in the performance of the DWF in retrieval rate. The best level of decompositions were found to be 3, as increasing the levels after this level tends to increase the computation time while not having much improvement in retrieval rate. The choice of wavelet basis for decomposition was found

Figure 4.22: Examples of retrieval result for VisTex database. The query is located at the top left.

to be not critical in retrieval performance, except for the Haar wavelet which gave quite a low rate compared to the others. The Daubechies 8-tap wavelet was however chosen as the wavelet basis for later experiments as it is known to have the best localization in the spatial and frequency domain. The choice of image padding was also found to be not critical, although in order to preserve the translation invariance properties of the DWF, the periodic padding was chosen. A simple pre-processing of removing the mean of the grey level before applying the wavelet decomposition is important in tex- ture retrieval as it helps in grouping together different textures with a rather similar configuration. Finally the best distance metric for use with the DWF features is the normalized Euclidean distance.

The next experiments focused on improving the retrieval accuracy of the DWF tech- niques. Several statistical functions were short-listed for evaluation and the standard deviation energy was found to have the best discrimination ability. Combining two or more statistical functions also helps in dramatically improving the retrieval accuracy, where an improvement of up to 9% was observed. However combining three or more statistical functions does not seem to have any improvement over the best combination of two functions only. Therefore the best combination of two statistical functions, which is the standard deviation energy and zero-crossings combination was chosen as the best combination. These two functions are computed from each of the DWF channels to obtain the best result. Dropping one or more channels seems to also reduce the retrieval rate.

Finally the final discrete wavelet frames features recorded a recognition rate of more than 80% for 100 Brodatz textures and almost 70% for 142 VisTex textures. In converting the colour images to grey scale, most of the conversion formulae tested give a very similar performance, indicating that the choice of conversion is not crucial. In the next chapters, the final discrete wavelet frames features presented in this chapter will be used in block -oriented decomposition as well as texture segmentation.

Block Oriented Decomposition

This chapter is concerned with the effectiveness of a block-oriented decomposition in texture retrieval. A brief review of some of the popular block-oriented decomposition techniques is presented and a novel block oriented decomposition technique is proposed. Experiments to evaluate the technique are conducted on a Brodatz database as well as real museum collections.

5.1

Introduction

A common visual query to an image database system involves finding all images in the database which contain a sub-image similar to a given query. As the feature vector of a complete database image may not correctly represent its sub-images, retrieval based on comparison between the feature vectors of the query image and database images may not provide satisfactory results. Thus, image segmentation is necessary to properly implement such feature-based techniques for searching image databases (124). Effective segmentation isolates the important features of database images.

Ideally, the results generated through the process of image recognition and analysis would be used to automatically segment image content. However, as image recognition itself is still in its formative stage, investigations in this direction are still in their infancy. In terms of texture retrieval, the texture property is usually computed in local masks for localization (36). It is therefore interesting to see if texture segmentation is really needed in the query by texture application, or whether the local masks approach mentioned above is just as effective. As far as texture retrieval is concerned, there is no study comparing the advantages and disadvantages of the two approaches in an open image retrieval application.

This chapter will therefore focus on the block-based approach while the following chapter will focused on the texture segmentation approach. The next section briefly describes

some available methods in using block-oriented image decomposition in texture locali- sation within an image.