翻译

外 文 翻 译 毕业设计题目： 基于多特征融合的彩色图像分类算法研究与实现

原文 1： Rapid and brief communication Active learning for image retrieval with Co-SVM

译文1： 快速和简单沟通主动学习与Co-SVM图像检索 2

原文1 Rapid and brief communication Active learning for image retrieval with Co-SVM Author：Kong qiao Wang ，Jian Cheng Nationality：China Originate from：DBLP

Abstract In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning algorithm, Co-SVM, to improve the performance of selective sampling in image retrieval. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifiers are learned in color and texture feature sub spaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples which are differently classified by the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval.

1. Introduction Relevance feedback is an important approach to improve the performance of image retrieval systems [1]. For large scale image database retrieval problem, labeled images are always rare compared with unlabeled images. It has become a hot topic how to utilize the large amounts of unlabeled images to augment the performance of the learning algorithms when only a small set of labeled images is available. Tong and Chang proposed an active learning paradigm, named SVM Active [2]. They think that the samples lying beside the boundary are the most informative. Therefore, in each round of relevance feedback, the images that are closest to the support vector boundary are returned to users for labeling. Usually, the feature representation of an image is a combination of diverse features, such as color, texture, shape, etc. For a specified example, the contribution of different features is significantly different. On the other hand, the importance 3

of the same feature is also different for different samples. For example, color is often more prominent than shape for a landscape image. However, the retrieval results are the averaging effort of all features, which ignores the distinct properties of individual feature. Some works have suggested that multi-view learning can do much better than the single-view learning in eliminating the hypotheses consistent with the training set [3,4]. In this paper, we consider color and texture as two sufficient and uncorrelated feature representations of an image. Inspired by SVM Active, we proposed a novel active learning method, Co-SVM. Firstly, SVM classifiers are separately leamt in different feature representations and then these classifiers are used to cooperatively select the most informative samples from the unlabeled data. Finally, the informative samples are returned to users to ask for labeling.

2. Support vector machines Being an effective binary classifier, Support Vector Machines (SVM) is particularly fit for the classification task in relevance feedback of image retrieval [5]. With the labeled images, SVM learns a boundary (i.e., hyper plane) separating the relevant images from the irrelevant images with maximum margin. The images on a side of boundary are considered as relevance, and on the other side are looked as irrelevance. Given a set of labeled images (x1, y1), . . . , (x n, y n), xi is the feature representation of one image, yi ∈ {−1,+1} is the class label (−1 denotes negative and +1 denotes positive). Training SVM classifier leads to the following quadratic optimization problem: njninixjxijkiyiyjiW111)},(2/1min{)(min

S.t．：,0,,01Ciiiyini where C is a constant and k is the kernel function. The boundary (hyper plane) is ,0)(bxw

Where nixsxrxsxrwbixiyiw1,];[2/1, are any support vectors satisfied: .1,1,0,ysyrsi The classification function can be written as

ibxxikiyisignxf).)(()( 4

3. Co-SVM 3.1. Two-view scheme It is natural and reasonable to assume that color features and texture features are two sufficient and uncorrelated views of an image. Assume that x = {c1, . . . , c i, t1, . . . , t j } is the feature representation of an image, where {c1, . . . , c i } and {t1, . . . , tj } are color attributes and texture attributes, respectively. For simplicity, we define the feature representation space V = VC × VT , and {c1, . . . , c i} ∈ VC, {t1, . . . , t j} ∈ VT . In order to find relevant images as much as possible, like the general relevance feedback methods, SVM is used to learn a classifier h on these labeled samples with the combined view V at the first stage. The unlabeled set is classified into positive and negative by h. Then m positive images are returned to user to label. At the second stage, SVM is used to separately learn two classifiers h C and h T on the labeled samples only with color view VC and texture view VT , respectively. A set of unlabeled samples that disagree between the two classifiers is recommended to user to label, which named contention samples. That is, the contention samples are classified as positive by h C (CP) while are classified as negative by h T (TN), or are classified as negative by h C (CN) while are classified as positive by h T (TP). For each classifier, the distance between sample and the hyper plane (boundary) can be looked as the confidence degree. The larger the distance, the higher the confidence degree is. In order to ensure that users can label the most informative samples, the samples which are close to hyper plane in both views are recommended to user to label.