Key Techniques Studying On Image Classification

Along with the development and abroad application of digital information aquiring techniques, the number of digital images and digital videos has grown enormously. Image classification task is developed under this kind of background and composed of image preprocessing, feature extraction and classifying processing three steps. Each step has some important affects on the final classification results. This paper just focuses on this side, analyzes the key techniques of those steps in image classification in turn. And effect methods have been proposed with each step's processing tasks:(1)Aim at the light impact for image classification, we propose an automatic truncation stretch and fast multi-scale Retinex method (TWMSR). Through automatic truncation stretch method, repair the distortion of MSR caused by max or min pixels during the process of mapping from log space to gray space; propose a fast window size irrelevant mean filtering method to substitute the Gaussian environment function and improve the computing speed of MSR. Comparing the TWMSR method with several other illumination normalizing and color constancy methods and prove that TWMSR method has better performance in illumination normalizing and color constancy.(2) For the popuse of getting rid of the noise impact for image classification, a gradient symmetrically changing bilateral filtering de-noising method (GSBF) is proposed. By analyzing the characteristics of image composing we propose the decision rules of symmetrically changing of image; combining the gradient symmetrically changing with bilateral filtering method. And then rebuild the filtered image iteratively using rules of symmetrically changing. Compare the GSBF method with some other de-noising methods, and give an analyzing on the stability of features. It proves that GSBF method can remove the noise more effectively and can get more stable features than others.(3)Aim at feature region capturing, a max distributing entropy wavelet-based salient multi-scale region detector (WSMR) has been proposed. It controls the features distributing characteristics efficiently by using max distributing entropy; by introducing multi-scale log space it can get wavelet feature regions with different scales. Evaluate the proposed WSMR and some other feature region detection methods on scale, blur, rotation, light and view angle. Use repeatability criterion to compare those methods and combine them with relative criterion between those features detected by those methods, bring forward the unite feature region detection method. By experiment it has shown that the unite feature region detection method fulfills the diversity of feature regions.(4)Through the analyzing on the expressed information carried by different descriptors, united descriptor method is constructed with several discriptors which have supplement informations for each other. Aiming at the characteristic of united descriptor, a Recall-precision evaluating method based on score rules is proposed. Compare the unite descriptor with some other descriptors and prove that the united descriptor can give the feature region a more stable expression. Further more, according to the comparing of chosen descriptors on several angles of image characteristics changing, the weighted coefficients of unite descriptors are decided.(5)By alalyzing the principle of visual words in image classification, an improved K-means clustering algorithm is proposed to generate visual words. Then give the algorithms analyzing of probabilistic latent semantic analysis (pLSA) model and Bayesian decision classification model based on Gaussian mixture model. By Experiment on those algorithms discussed in the paper and concluded that the improvement in each step can bring up the image classification effect and can also make better performance in corresponding image processing function.