Hai Li

Bio: Hai Li is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Feature (computer vision) & Image retrieval. The author has an hindex of 2, co-authored 3 publications receiving 12 citations.

A Novel Multi-Feature Fusion and Sparse Coding- Based Framework for Image Retrieval

Abstract: In traditional image retrieval techniques, the query results are severely affected when the images of varying illumination and scale, as well as occlusion and corrosion. Seeking to solve this problem, this paper proposed a novel multi-feature fusion and sparse coding based framework for image retrieval. In the framework, firstly, inherent features of an image are extracted, and then dictionary learning method is utilized to construct them to be dictionary features. Finally, the proposed framework introduces sparse representation model to measure the similarity between two images. The merit is that a feature descriptor is coded as a sparse linear combination with respect to dictionary feature so as to achieve efficient feature representation and robust similarity measure. In order to check the validity of the framework, this paper conducted two groups of experiments on Corel-1000 image dataset and the Stirmark benchmark based database respectively. Experimental results show that the proposed framework is much more effective than the state-of-the-art methods not only in traditional image dataset but also in varying image dataset.

Affine invariant features-based tone mapping algorithm for high dynamic range images

Abstract: Conventional digital display devices, due to their hardware limitations, can't represent the whole range of luminance in High Dynamic Range (HDR) images. In order to solve this incompatible problem, many tone mapping techniques were introduced to reproduce HDR images. Unlike the traditional methods applied in standard scale space, this paper proposes a novel affine invariant features-based tone mapping algorithm in affine Gaussian scale space. The reason of using this scale space is due to the fact that it is able to extract anisotropic feature regions in addition to traditional isotropic feature regions. Firstly, the proposed method extracts the anisotropic features from HDR images and reforms them to be isotropic by Fitting & Affine transformation. Then, dodging-and-burning processing is utilized to obtain base layer of HDR images. Finally, two-scale edge-preserving decomposition is employed to generate detail layer of HDR images and combine two layers to produce output images. Experimental results show that the proposed algorithm outperforms previous methods for reproducing the real scene of HDR images, especially for large perspective and scale transformed images which contain considerable anisotropic feature regions.

Image retrieval system and image retrieval method based on multi-feature and sparse representation

Abstract: The invention discloses an image retrieval system and an image retrieval method based on multi-feature and sparse representation. The system comprises a feature extraction module, a feature dictionary building module, a similarity measurement module, an information storage module and an inquiry interaction module, wherein the feature extraction module adopts shape and color combining image features, color enhanced Gaussian Laplace features (CLOG features) and SURF features; the feature dictionary building module compresses original features into overcomplete overcomplete features through an on-line dictionary learning algorithm, and overcomes the defect that the original features are too dense; the similarity measurement module introduces a sparse representation theory, compares the residual error size generated by original dictionary and relevant dictionary representation, and judges the similarity of the two images, and the problem of higher feature dependence of the traditional similarity measurement method is solved. The system and the method provided by the invention have the advantages that rotating, noise and illumination change images can be effectively retrieved, and the image retrieval robustness is obviously improved.

Separable vocabulary and feature fusion for image retrieval based on sparse representation

Abstract: Visual vocabulary is the core of the Bag-of-visual-words (BOW) model in image retrieval. In order to ensure the retrieval accuracy, a large vocabulary is always used in traditional methods. However, a large vocabulary will lead to a low recall. In order to improve recall, vocabularies with medium sizes are proposed, but they will lead to a low accuracy. To address these two problems, we propose a new method for image retrieval based on feature fusion and sparse representation over separable vocabulary. Firstly, a large vocabulary is generated on the training dataset. Secondly, the vocabulary is separated into a number of vocabularies with medium sizes. Thirdly, for a given query image, we adopt sparse representation to select a vocabulary for retrieval. In the proposed method, the large vocabulary can guarantee a relatively high accuracy, while the vocabularies with medium sizes are responsible for high recall. Also, in order to reduce quantization error and improve recall, sparse representation scheme is used for visual words quantization. Moreover, both the local features and the global features are fused to improve the recall. Our proposed method is evaluated on two benchmark datasets, i.e., Coil20 and Holidays. Experiments show that our proposed method achieves good performance.

Review of image low-level feature extraction methods for content-based image retrieval

Abstract: In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.,First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.,The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.,A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

Image categorization using non-negative kernel sparse representation

Abstract: Sparse representation of signals have become an important tool in computer vision In many computer vision applications such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performances Sparse representation models often contain two stages: sparse coding and dictionary learning In this paper, we propose a non-linear non-negative sparse representation model: NNK-KSVD In the sparse coding stage, a non-linear update rule is proposed to obtain the sparse matrix In the dictionary learning stage, the proposed model extends the kernel KSVD by embedding the non-negative sparse coding The proposed non-negative kernel sparse representation model was evaluated on several public image datasets for the task of classification Experimental results show that by exploiting the non-linear structure in images and utilizing the ‘additive’ nature of non-negative sparse coding, promising classification performance can be obtained Moreover, the proposed sparse representation method was also evaluated in image retrieval tasks, competitive results were obtained

Fusion of multiple VLAD vectors based on different features for image retrieval

Abstract: Traditional VLAD method only uses the SITF feature. Since the SITF feature represents the local gradient information, thus VLAD representation based on SITF feature of image has low discriminative power. To address the problem, we present a simple and effective method that fuse the VLAD vectors based on local gradient and color information. Also, in order to improve the retrieval accuracy and reduce running time, we use whitening operator for VLAD vectors. Our proposed method is evaluated on two benchmark datasets, i.e., Holidays and Ukbench. Experiments show that our proposed method achieves good performance.

Color-Based Image Retrieval Using Proximity Space Theory

Abstract: The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.