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In content-based image retrieval, relevance feedback is an effective approach to narrow the semantic gap between low-level feature and high-level semantic interpretation by using user's feedback to judge the relevance images in the retrieval process. One important issue of RF-algorithms is how to efficiently and effectively select the helpful unlabelled samples for labelling so that the retrieval...
We propose a spatial-color layout feature specially designed for galaxy images. Inspired by findings on galaxy formation and evolution from Astronomy, the proposed feature captures both global and local morphological information of galaxies. In addition, our feature is scale and rotation invariant. By developing a hashing-based approach with the proposed feature, we implemented an efficient galaxy...
This paper describes a novel method for extracting features of batik images. This method is called enhanced micro-structure descriptor (EMSD). EMSD is the enhanced model of micro-structure descriptor (MSD) which proposed by Guang-Hai Liu. Different with MSD that uses only edge orientation similarity for creating micro-structure map and then utilises this map along with color values; EMSD adds a new...
Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique is used in image retrieval system to organize and locate images of interest from a database. Many techniques have been proposed for image annotation in the last decade that gives reasonable performance...
Although many region based models for image auto-annotation have been proposed recently, their performances are not satisfactory due to the sensitivity to segmentation errors. In this paper, by evaluating two image partition methods and four visual features, we propose a new ensemble method under Multi-Instance Multi-Label (MIML) learning framework which has been proposed recently. The ensemble method...
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel...
With the repaid development of internet technology, image documents have become an important information source. It is hard for us to retrieve certain images from all available ones. In this paper, we propose an interactive image recommendation system, which firstly uses color histogram feature or Gabor texture feature to express image contents, then a kernel based K-meanse is utilized to cluster...
How to reduce more of the image dimensions without losing the main features of the image is highlighted in the research of Web content-based image retrieval. This paper started by analysis of commonly used methods for the dimension reduction of Web images, followed by proposing dimensionality reduction method that is based on HSV features, where the HSV color histogram intersection was used as the...
Human identity recognition is an important yet under-addressed problem. Previous methods were strictly limited to high quality photographs, where the principal techniques heavily rely on body details such as face detection. In this paper, we propose an algorithm to address the novel problem of human identity recognition over a set of unordered low quality aerial images. Assuming a user was able to...
In this paper, a new image retrieval method, which uses the two primary features of image cluster to retrieve image, i.e., the average color value and the number of pixels for each cluster, is proposed. In comparison with popular methods, since this method uses both color feature and structure feature, it is good to solve such a sort of image retrieval questions for two images that they are similar...
Supervised learning requires adequately labeled training data. In this paper, we present an approach for automatic detection of outliers in image training sets using an one-class support vector machine (SVM). The image sets were downloaded from photo communities solely based on their tags. We conducted four experiments to investigate if the one-class SVM can automatically differentiate between target...
Support vector machines (SVM) has been widely applied in the area of content-based image retrieval in order to learn high-level concepts from low-level image features. Most existing SVM based image retrieval algorithms only rely on global-based features to represent the image content, which obviously can not well reflect the image semantic content. Region-based representations are far more close to...
Automatic semantic scene classification is a challenging research topic in computer vision and it is also a promising solution to scene understanding and image semantic retrieval. In this paper, novel techniques are proposed to implement multi-semantic scene classification. We first extract some regions of interest (ROIs) from each image based on image-driven, bottom-up visual attention model, and...
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