A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.     

用于图像检索的连通直方图方法

本文提出了一种用于图像检索的连通直方图（CRH：Connected-Region histogram)方法。连通直方图是图像中连通区域面积的统计函数，反应了图像中连通区域的概率分布，是图像内容的一种简洁表达。它具有灰度、旋转、平移等不变性，并可通过权函数来强调某些连通区域。另外，还可以根据连通区域的其他特征（如连通区域的形状、所处的相对位置等）来优化、改进连通直方图，提高图像的检索效率。实验证明，连通直方图具有一定的图像检索能力。     

基于信息熵和Gabor滤波器的图像检索

采用颜色和纹理的多特征提取,将图像按照一定的规则进行分块,对各个分块分别进行各种特征向量的提取.采用HSV颜色空间把颜色特征量化到72个颜色空间得到72柄的一维直方图,计算图像信息熵;纹理特征采用Gabor滤波器.该种分块方法能够很好的利用图像内容的空间信息,综合颜色和纹理特征能够有效地提高查全率和差准率.     

基于中文自然语言的纹理分类新方法

提出了一种新的基于中文自然语言纹理描述词的纹理分类方法,建立了自然纹理分类体系,并用最小二乘支持向量机对纹理进行分类,实现了纹理的视觉特征到语义描述的转换.实验结果证明,该方法在图像理解和基于内容的图像检索中有助于缩小纹理特征的数学描述和人类理解之间的"语义鸿沟".     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

基于DCT压缩域的图像检索方法

本文提出了一种基于DCT(Discrete Cosine Transform)压缩域的图像检索方法.对于DCT编码的图像数据,在不需要完全解码的情况下,直接抽取图像的内容特征进行图像检索.首先,重组DCT域的频率系数,使其具有方向性、多分辨率等特点,并利用这些特点提取图像的大致轮廓.接着统计图像轮廓的连通直方图(CRH:Connected-Region Histogram),进行图像检索.并利用DC图的灰度直方图对检索结果进行重新排序.这种检索方法对灰度、旋转、平移等都具有一定的鲁棒性,具有较好的检索效果.     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.     

Expert content-based image retrieval system using robust local patterns

A new image indexing and retrieval algorithm for content based image retrieval is proposed in this paper. The local region of the image is represented by making the use of local difference operator (LDO), separating it into two components i.e. sign and magnitude. The sign LBP operator (S_LBP) is a generalized LBP operator. The magnitude LBP (M_LBP) operator is calculated using the magnitude of LDO. A robust LBP (RLBP) operator is presented employing robust S_LBP and robust M_LBP. Further, the combination of Gabor transform and RLBP operator has also been presented. The robustness is established by conducting four experiments on different image database i.e. Corel 1000 (DB1), Brodatz texture database (DB2) and MIT VisTex database (DB3) under different lighting (illumination) and noise conditions. Investigations reveal a promising achievement of the technique presented when compared to S_LBP and other existing transform domain techniques in terms of their evaluation measures.     

基于角度分类的快速图像检索系统

基于内容的图像检索是近年来计算机视觉领域的重要方向之一,如何快速准确地匹配视觉信息内容是图像检索最关键的部分。目前大多数检索方法采用BOF(bag of features)算法,该算法的检索精度较低,且运行速度较慢。提出了一种新的匹配方法,提高检索精度的同时有效减少了检索时间。本算法利用特征点的四个相对独立的角度对其进行分类,可大幅减少需要比较的特征算子的数量,并对每一分类中的特征点使用k-means算法聚类,得到若干个聚类中心。本方法对每一聚类的特征点进行汉明编码,并采用倒排表的方式进行信息存储。实验对象使用Holiday图像库,结果显示,检索精度和检索速度较原先算法得到了较大程度的改善,检索精度最高可提高55.9%,至0.8557,检索时间最多可降低49.3%,至0.35s。