Objective

2

The extraction of image features plays an important role in visual tracking and is an essential component of image matching and other image processing applications. Different image description methods can affect the performance of the algorithm directly. Domestic and foreign researchers have proposed numerous image features

which can be divided into two classes as follows: 1) deep features based on deep learning

which have obtained successful effects but need substantial data to train the model and have a great demand on the experimental platform that has several restrictions on its applications to a great extent; 2) traditional manual features

which can be performed on any existing platform that is simple and intuitive. They have fulfilled remarkable results in image processing and include scale-invariant feature transform

histogram of oriented gradient

and color name (CN). Thus

further studying manual features is essential. Nevertheless

improving the performance of the algorithm by only depending on a single feature is difficult. In this study

a hierarchical structure histogram combined with CN is proposed to overcome the problem in which a single color feature is susceptible to illumination changes

thus leading to poor robustness

and the problem in which the spatial structure of the image is sensitive to the deformation of the target

which will reduce the distinguishability of features.

Method

2

Aimed at the disadvantage in which the pixel gray value is susceptible to the layered image with illumination change

an improved method based on CN to the layer image is proposed in this work. First

the method projects the original RGB color space to a robust color space-color name space

and objects are represented by a probabilistic 11-dimensional map

which means that the input image is stratified to 11 layers according to CN. Second

each pixel in the image is projected into the layer with the highest probability so that the pixels intersect with each other to be empty and are integrated into the entire image. That is to say

each pixel can only be projected into one layer. Furthermore

for every hierarchical image

the method calculates the distribution of pixels in every dimension by counting the number of pixels in each square of structure image element then obtains the spatial distribution information of pixels. Finally

the pixel spatial information of each slice is connected in series as a hierarchical histogram to represent the image.

Result

2

To prove the validity and strong distinguishability of the proposed feature

two experiments are performed in this study. The first is image matching

whose strategy consists of an extant model. The position of the matched image is determined by traversing the original image exhaustively. The data set of image matching is from PASCAL VOC2007 (visual object classes)

which contains various classes of objects (e.g.

person

bird

car

and dog). When calling this data set

only the target pointed by the first ground truth of each image is used. The second one is visual tracking

whose tracking frame adopts particle filter

and the number of particles is 200. This experiment is evaluated on 100 sequences of object tracking benchmark (OTB100)

which mainly contains 11 challenges (e.g.

out-of-plane rotation

scale variation

and illumination variation) that may be encountered in object tracking. All experiments in this study are run on the Windows platform

and the development environment is MATLAB. We compare this feature against four traditional manual features

and experimental results show that image matching based on this features can locate the target accurately

distinguish similar targets well

and the peak value of the target is obvious. In the meantime

the mean value of peak-to-side lobe ratio increases by 1.347 9. Moreover

object tracking based on this feature can considerably reduce the center location error of tracking and improve the accuracy and success rate momentously. The success rate increases by 4%

and the accuracy increases by 4.6%.

Conclusion

2

Hierarchical structure histogram based on the pixel gray value is easily affected by illumination and target rotation changes. Thus

in this study

we adopt a new method to layer images to gain robust features. The proposed feature combines CN features with spatial information of pixels in images to improve the capability of features to adapt to various scenes

such as illumination changes

deformation

and low resolution

and it effectively enhances the poor discrimination of a single feature and makes it more robust in image matching and object tracking

especially for the objects with the same color distribution but different spatial distributions of pixels. In addition

this feature retains the representation of traditional histogram features

thus making computation and similarity measurement simple. Compared with four traditional manual features

this feature exhibits better image matching performance and visual tracking results in most cases. Therefore

this feature can be effectively applied to image processing applications. However

this feature still has deficiencies

such as not considering the problem of target scale change in the visual tracking process. In follow-up work

we will further optimize the feature to obtain enhanced generalization capabilities in visual tracking and to remain applicable to other image processing applications simultaneously.