Feature selection in mixed data

Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.

Image Features Detection, Description and Matching

Content-based image retrieval (CBIR) systems require users to query images by their low-level visual content; this not only makes it hard for users to formulate queries, but also can lead to unsatisfied retrieval results. To this end, image annotation was proposed. The aim of image annotation is to automatically assign keywords to images, so image retrieval users are able to query images by keywords. Image annotation can be regarded as the image classification problem: that images are represented by some low-level features and some supervised learning techniques are used to learn the mapping between low-level features and high-level concepts (i.e., class labels). One of the most widely used feature representation methods is bag-of-words (BoW). This paper reviews related works based on the issues of improving and/or applying BoW for image annotation. Moreover, many recent works (from 2006 to 2012) are compared in terms of the methodology of BoW feature generation and experimental design. In addition, several different issues in using BoW are discussed, and some important issues for future research are discussed.

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Bag-of-Words Representation in Image Annotation: A Review

Fast image classification by boosting fuzzy classifiers

Deep sparse feature selection for computer aided endoscopy diagnosis

Discriminative features for image classification and retrieval

In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pair wise image matching scheme to select the discriminative features. Only the label information from the raining-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images thus highlight the high level category knowledge of images. "Visual words" are constructed on these selected features. Our method can be used as a refinement step for current image classification and retrieval process. We prove the efficiency of our methods in three tasks: supervised image classification, semi-supervised image classification and image retrieval. In the experiment part, we use two canonical datasets Caltech 256 and MSRC-v2 to validate our method. The results show that the share of discriminative features increases significantly to 87% by applying our selection. In four group of contract tests, supervised classification accuracies are shown to improve up to 21% while semi supervised classification accuracies are 15%. Image retrieval precision is also remarkable enhanced by our method.

Discriminative Features for Image Classification and Retrieval

In this chapter, the authors present a new method to improve the performance of current bag-of-words based image classification process. After feature extraction, they introduce a pairwise image matching scheme to select the discriminative features. Only the label information from the training-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images, and thus highlight the high level category knowledge of images. Visual words are constructed on these selected features. This novel method could be used as a refinement step for current image classification and retrieval process. The authors prove the efficiency of their method in three tasks: supervised image classification, semi-supervised image classification, and image retrieval. DOI: 10.4018/978-1-4666-1891-6.ch011

Shang Liu

Papers

Discriminative features for image classification and retrieval

Discriminative Features for Image Classification and Retrieval

Discriminative Feature Selection in Image Classification and Retrieval