Visual Object Representation Based On Salient Local Features

Visual object representation bridges the gap between low-level image features and high-level semantic concepts. It plays an important role in computer vision tasks such as image recognition, scene understanding, and etc. Visual object representation based on local features has advantages of expressiveness and robustness, and attracted a lot of attention in recent years. This dissertation focuses on the statistical modeling and discriminative learning of category local features for visual object representation, including the statistical modeling and discriminative learning of visual words, category salient local feature detection, and the cooperation between category and universal visual words. By statistically modeling visual words, the accuracy of visual object representation can be improved. Through detecting category salient local features, object positions in images can be rapidly located. Using the cooperation between category and universal visual words, the better discriminative ability will be brought to object classifiers.An approach to the statistical modeling and discriminative learning of visual words is proposed. The distribution of local features from each visual word is assumed as the Gaussian mixture model (GMM) and learned from the training data by the Max-Min posterior Pseudo-probabilities (MMP), a discriminative learning method. The similarities between each visual word and corresponding local features are computed, summed up, and normalized to construct a soft-histogram. Two representation methods are considered in the object recognition experiments, to evaluate the proposed algorithm. The first one is called classification-based soft histogram, in which each local feature is assigned to only one visual word with maximum similarity. The second one is called completely soft histogram, in which each local feature is assigned to all the visual words. The experiments are conducted in Caltech-4 and PASCAL VOC 2006 databases.An algorithm is presented to detect category salient local features in images. We consider category appearance saliency as well as category context saliency of local features. Firstly, the category appearance saliency of a local feature is determined by the posterior probability of being a specific object category. Then, the local features with category appearance saliency are verified by contextual information in their neighborhood. Actually, a co-occurrence star model is constructed to measure category context saliency of local features based on the co-occurrence relationship between visual words. We apply the proposed algorithm to object localization and recognition. The experimental results on INRIA horse, PASCAL VOC 2006, and Caltech-101 datasets show that our algorithm brings better efficiency and effectiveness of object localization and improves the accuracy of object recognition.An image representation and classification algorithm with the cooperation between category and universal visual words is described. Category visual words are generated by assuming that local features in training images of a class are of a distribution of GMM. The number of visual words for a class is automatically determined by the minimum description length criterion. All the category visual words are clustered to obtain universal visual words. A category-specific image representation is defined by employing the cooperation between two types of visual words. The resultant feature vectors of an image vary with different classes, including their dimensionalities and elements. We integrate the proposed method into the MMP learning to perform image classification. The corresponding image classifier is evaluated in its applications to object categorization and automatic image annotation. Experimental results on PASCAL VOC 2006, Caltech-101 and Corel-5K datasets show that the proposed method is effective and promising.