Research On Image Semantic Classification Method Based On Salient Regions

As digital image acquisition devices are used widely, the number of images increases exponentially. How to organize, classify and retrieve digital image has become a valuable research topic. Based on image salient regions, this paper mainly focuses on the approaches of bag-of-visual words construction, image dissimilarity measure and image semantic classification, and tests the classification algorithm.The main content of this research is as follows:(1) Since it is relatively difficult to distinguish different classes with the whole image features which include too much redundant information, we propose an image bag-of-visual word construction approach. To begin with, Harris-Laplace region detector is employed to acquire image salient regions, which are described to form the corresponding feature vectors by feature descriptors. Subsequently, affinity propagation algorithm is used to cluster the image salient regions. Finally, the exemplar of each cluster is regarded as visual word, while the proportion of the number of feature vectors in a cluster to that of feature vectors in the whole image is regarded as the corresponding frequency. The exemplar and the corresponding frequency are formed bag-of-visual word. Experimental results show that bag-of-visual word could express the main information of an image.(2) In order to measure the dissimilarity between two images more reasonably, we propose a bag-of-visual word based EMD (Earth Mover's Distance) dissimilarity measure approach, which regards visual word as the bin of a histogram and the frequency of the visual word as the statistics information on the corresponding bin of the histogram. First of all, a dissimilarity matrix is constructed, which saves Euclidean distances between two image bag-of-visual words. Subsequently, an only existed flow is found for two image bag-of-visual words by subjecting to some constraints. Finally, the dissimilarity of two images is acquired. Experimental results show that this dissimilarity approach could measure the dissimilarity between two images relatively reasonably, and have great impact on image semantic image classification.(3) We propose a multi-descriptor multi-nearest neighbors image classification algorithm to address image semantic classification problems. First of all, for each image Harris-Laplace salient region detector is used to detect salient regions which are described by different image feature descriptors to form feature vectors. Subsequently, bag-of-visual word is constructed by using affinity propagation algorithm, and the nearest neighbors of an unlabeled image coming from all categories are found by bag-of-visual word based EMD dissimilarity measure approach. Finally, unlabeled images could be classified by combing different feature descriptors and using the results of dissimilarity measures between unlabeled images and their corresponding nearest neighbors.(4) Experiments are done on two renowned image database, i.e., 1000-image and Caltech-101 Object, to evaluate our classification algorithm by using Matlab, Java and C++ programming languages. Experimental results on 1000-image database show that compared to using only one kind of feature descriptor, the approach of using multi image feature descriptors could boosts mean recognition rates of image classification performance from 5% to 30%. Meanwhile, experimental results on Caltech-101 Object database show that when the number of labeled images is small, our image classification algorithm outperforms some state-of-the-art algorithms, and when the number of labeled images is large, our image classification algorithm performs almost the same as some state-of-the-art algorithms.