Study On Several Issues Of Content-based Image Retrieval

A picture is worth a thousand words. Images play a very important role in human daily activities. Recently with the development of the Internet technology and popularization of the consumer electronic devices(such as mobile phone, digital camera, etc.), enormous digital images are created, distributed and shared everyday. It is a challengable task to search images rapidly and efficiently in all kinds of multimedia image databases. However, most of current image search engines(such as Google^TM and Baidu^TM) search images not by images' contents, but by texts related to them(for example, texts related to images in a web page), which leads to the poor retrieval accuracy. Content-based image retdeval(CBIR) is the key technique to solve the problem that is how to retrieve useful information within enormous amount of digital images. In this paper, several issues were studied.Image texture feature is a widely used low-level visual feature in CBIR society. In this paper, image texture was regarded as the signal generated by non-linear dynamic systems. Two non-linear signal analysis approaches, whose names are time series complexity approach and the Hilbert-Huang Transform(HHT), were used to extract the texture feature from images. The extracted texture features were used for image retrieval experiments.The time series complexity approach was applied to image retrieval. Following conclusions were drawed: Image retrieval results were compared based on eight different time series complexity algorithms, which leaded to a conclusion that symbol dynamic-based and entropy-based complexity algorithms are not suitable for image retrieval. A complexity approach named CO complexity, which is based on Fourier spectrum analysis, is suitable for image retrieval. The CO complexity-based retrieval results are relevant to the scanning methods which convert image from two-dimensional structure to one-dimensional time seres form. Experimental results showed that the retrieval accuracy based on CO complexity algorithm with Hilbert scanning method is comparable to that based on Gabor feature, which is an excellent texture feature in image retrieval. Moreover, consuming time to extract the CO complexity-based texture feature is far shorter than that of the Gabor-based texture feature.Motivated by the image binarized algorithms, a novel time series coarse graining framework was proposed.Several texture features based on 2D-CO complexity measurement were proposed, whose names were complexity histogram and multi-scale complexity histogram, complexity co-occurrence matrix, complexity texture spectrum and multi-scale complexity feature. Experimental results showed that multi-scale complexity feature based on pyramid decomposition is a favourable texture feature.The approach of texture image decomposition and texture feature extraction based on HHT, which can decompose the image into a set of functions denoted Intrinsic Mode Functions(IMF) and a residue, was presented. The extracted features were used for texture image retrieval. The Bidimensional Empirical Mode Decomposition(BEMD) method was used to decompose the texture image, the features extracted were the mean and standard deviation of the amplitude of the IMFs and their Hilbert transformations. Furthermore, according to the spatial relationship between local extrema points, a novel boundary processing approach based on clustering algorithm was proposed. Preliminary comparision experimental results showed that the texture image retrieval results based on HHT were encouraged.Salient region of the natural image is the main part to describe the image semantic, which is called ROI(region of interesting). In this paper, a saliency-based bottom-up visual attention computational model which was motivated by visual physiological and psychophysical experimental results was used for natural image retrieval.Interesting points in natural images were selected by using the visual attention computational model. Furthermore, local features around the interesting points were computed for natural image retrieval. The proposed local salient features were called image salient histogram, image salient signature and focus of attention(FOA) spatial relationship histogram. Experimental results showed that salient local feature which combine salient signature and FOA spatial relationship histogram can achieve better retrieval accuracy than global feature.In this paper, natural image retrieval approach based on visual attention computational model and latent semantic indexing was also proposed.Multi-instance learning(MIL) is a new machine learning framework which has the ability of "learning from ambiguity". MIL may be a hopeful approach to solve the difficult "semantic gap" problem in image retrieval. Designing good bag generator is an important problem in MIL. Two novel bag generators based on visual attention computational model and an effficient image segmentation algorithm whose name is JSEG were proposed. Natural image retrieval experiments were carded based on MIL and these two bag generators. Experimental results showed that bag generator based on JSEG algorithm can achieve better retrieval results than other bag generators introduced in some literatures.The image perception threshold and its metric approach was proposed. Problem of the image perception threshold was studied on a natural image database based on image color/gray mode and different image transformation approaches by using computer and psychophysical experiments. The image transformation approaches used were scale transformation, Gaussian noise transformation and motion blurred transformation. According to the preliminary experimental results, following conclusions were drawed: There exists a perception threshold when human perceive the natural images. The image perception threshold can be measured by image entropy and image fractal dimension number. There exists a law similar with the Weber's law when human perceive the natural images. The ratio of metric of the difference image to that of the original image is independent of the image content and is relevant to both image color/gray mode and image transformation approaches.A general-purpose image retrieval experimental platform was also developed. Reseachers can use this platform not only to carry out all kinds of image retrieval experiments without any coding, but also do academic intercommunion conveniently. This platform is very useful for image retrieval study.