Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

     

Automatic content-based retrieval and semantic classification of video content

The problem of video classification can be viewed as discovering the signature patterns in the elemental features of a video class. In order to solve this problem, a large and diverse set of video features is proposed in this paper. The contributions of the paper further lie in dealing with high-dimensionality induced by the feature space and in presenting an algorithm based on two-phase grid searching for automatic parameter selection for support vector machine (SVM). The framework thus is directed to bridge the gap between low-level features and semantic video classes. The experimental results and comparison with state-of-the-art learning tools on more than 5000 video segments show the effectiveness of our approach.     

A semantic modeling approach for image retrieval by content

We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach.     

Feed-forward content based image retrieval using adaptive tetrolet transforms

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.     

Edge based features for content based image retrieval

The common problem in content based image retrieval (CBIR) is selection of features. Image characterization with lesser number of features involving lower computational cost is always desirable. Edge is a strong feature for characterizing an image. This paper presents a robust technique for extracting edge map of an image which is followed by computation of global feature (like fuzzy compactness) using gray level as well as shape information of the edge map. Unlike other existing techniques it does not require pre segmentation for the computation of features. This algorithm is also computationally attractive as it computes different features with limited number of selected pixels.     

基于隐含语义分析的音乐检索

为提高音乐检索效率,使检索结果与搜索目的更接近,提出了基于隐含语义分析的音乐检索方法.将曲谱表示为标准音符和音转的交替串,基于每个交替串使用频率高于包含它的多交替串排列的事实,设计了音乐词汇统计算法.为使各分句能整齐地转化为相同维数的向量,使用最长的分句长度作为标准维数,基于增加频率和的原则进行单词的重新分割.实验结果表明,基于隐含语义分析的检索能获得令人满意的检索结果.     

Automatic image annotation and retrieval using group sparsity

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, whereas properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization-based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. Using this group-sparsity-based method, the whole group of features [e.g., red green blue (RGB) or hue, saturation, and value (HSV)] is either selected or removed. Thus, we do not need to extract this group of features when new data comes. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus, keyword similarity is modeled in the annotation framework. We also show that our framework can be employed in image retrieval tasks by selecting different image pairs. Extensive experiments are designed to compare the performance between features, feature combinations, and regularization-based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group-sparsity-based method is more accurate and stable than others.     

An efficient bi-layer content based image retrieval system

Multimedia Tools and Applications - Large amount of multi-media content, generated by various image capturing devices, is shared and downloaded by millions of users across the globe, every second....     

A content based image information retrieval and video thumbnail extraction framework using SOM

Searching an image or a video in a huge volume of graphical data is a tedious time-consuming process. If this search is performed using the conventional element matching technique, the complexity of the search will render the system useless. To overcome this problem, the current paper proposes a Content-Based Image Retrieval (CBIR) and a Content-Based Video Retrieval (CBVR) technique using clustering algorithms based on neural networks. Neural networks have proved to be quite powerful for dimensionality reduction due to their parallel computations. Retrieval of images in a large database on the basis of the content of the query image has been proved fast and efficient through practical results. Two images of the same object, but taken from different camera angles or have rotational and scaling transforms is also matched effectively. In medical domain, CBIR has proved to be a boon to the doctors. The tumor, cancer etc can be easily deducted comparing the images with normal to the images with diseases. Java and Weka have been used for implementation. The thumbnails extracted from the video facilitates the video search in a large videos database. The unsupervised nature of Self Organizing Maps (SOM) has made the software all the more robust.

     

Automatic image annotation and retrieval using weighted feature selection

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.     

Automatic text summarization using latent semantic analysis

In the paper, the most state-of-the-art methods of automatic text summarization, which build summaries in the form of generic extracts, are considered. The original text is represented in the form of a numerical matrix. Matrix columns correspond to text sentences, and each sentence is represented in the form of a vector in the term space. Further, latent semantic analysis is applied to the matrix obtained to construct sentences representation in the topic space. The dimensionality of the topic space is much less than the dimensionality of the initial term space. The choice of the most important sentences is carried out on the basis of sentences representation in the topic space. The number of important sentences is defined by the length of the demanded summary. This paper also presents a new generic text summarization method that uses nonnegative matrix factorization to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state-of-the-art methods on the DUC 2001 and DUC 2002 standard data sets.     

An effective technique for the content based image retrieval to reduce the semantic gap based on an optimal classifier technique

Content Based Image Retrieval (CBIR) systems use Relevance Feedback (RF) in order to improve the retrieval accuracy. Research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the “semantic gap” between the visual features and the richness of human semantics. In this paper, a novel system is proposed to enhance the gain of long-term relevance feedback. In the proposed system, the general CBIR involves two steps—ABC based training and image retrieval. First, the images other than the query image are pre-processed using median filter and gray scale transformation for removal of noise and resizing. Secondly, the features such as Color, Texture and shape of the image are extracted using Gabor Filter, Gray Level Co-occurrence Matrix and Hu-Moment shape feature techniques and also extract the static features like mean and standard deviation. The extracted features are clustered using k-means algorithm and each cluster are trained using ANN based ABC technique. A method using artificial bee colony (ABC) based artificial neural network (ANN) to update the weights assigned to features by accumulating the knowledge obtained from the user over iterations. Eventually, the comparative analysis performed using the commonly used methods namely precision and recall were clearly shown that the proposed system is suitable for the better CBIR and it can reduce the semantic gap than the conventional systems.     

An endmember-based distance for content based hyperspectral image retrieval

We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.     

Sparse semantic metric learning for image retrieval

Typical content-based image retrieval solutions usually cannot achieve satisfactory performance due to the semantic gap challenge. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval. In this paper, we propose a sparse semantic metric learning (SSML) algorithm by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from the traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from two views of images and formulates the learning problem with the following principles. The semantic structure in the text space is expected to be preserved for the transformed space. To prevent overfitting the noisy, incomplete, or subjective tagging information of images, we expect that the mapping space by the learned metric does not deviate from the original visual space. In addition, the metric is straightforward constrained to be row-wise sparse with the ?_2,1-norm to suppress certain noisy or redundant visual feature dimensions. We present an iterative algorithm with proved convergence to solve the optimization problem. With the learned metric for image retrieval, we conduct extensive experiments on a real-world dataset and validate the effectiveness of our approach compared with other related work.     

Automatic image semantic interpretation using social action and tagging data

The plethora of social actions and annotations (tags, comments, ratings) from online media sharing Websites and collaborative games have induced a paradigm shift in the research on image semantic interpretation. Social inputs with their added context represent a strong substitute for expert annotations. Novel algorithms have been designed to fuse visual features with noisy social labels and behavioral signals. In this survey, we review nearly 200 representative papers to identify the current trends, challenges as well as opportunities presented by social inputs for research on image semantics. Our study builds on an interdisciplinary confluence of insights from image processing, data mining, human computer interaction, and sociology to describe the folksonomic features of users, annotations and images. Applications are categorized into four types: concept semantics, person identification, location semantics and event semantics. The survey concludes with a summary of principle research directions for the present and the future.     

Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine

In recent years, the rapid growth of multimedia content makes content-based image retrieval (CBIR) a challenging research problem. The content-based attributes of the image are associated with the position of objects and regions within the image. The addition of image content-based attributes to image retrieval enhances its performance. In the last few years, the bag-of-visual-words (BoVW) based image representation model gained attention and significantly improved the efficiency and effectiveness of CBIR. In BoVW-based image representation model, an image is represented as an order-less histogram of visual words by ignoring the spatial attributes. In this paper, we present a novel image representation based on the weighted average of triangular histograms (WATH) of visual words. The proposed approach adds the image spatial contents to the inverted index of the BoVW model, reduces overfitting problem on larger sizes of the dictionary and semantic gap issues between high-level image semantic and low-level image features. The qualitative and quantitative analysis conducted on three image benchmarks demonstrates the effectiveness of the proposed approach based on WATH.     

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.     

Enhanced independent component analysis and its application to content based face image retrieval.

This paper describes an enhanced independent component analysis (EICA) method and its application to content based face image retrieval. EICA, whose enhanced retrieval performance is achieved by means of generalization analysis, operates in a reduced principal component analysis (PCA) space. The dimensionality of the PCA space is determined by balancing two competing criteria: the representation criterion for adequate data representation and the magnitude criterion for enhanced retrieval performance. The feasibility of the new EICA method has been successfully tested for content-based face image retrieval using 1,107 frontal face images from the FERET database. The images are acquired from 369 subjects under variable illumination, facial expression, and time (duplicated images). Experimental results show that the independent component analysis (ICA) method has poor generalization performance while the EICA method has enhanced generalization performance; the EICA method has better performance than the popular face recognition methods, such as the Eigenfaces method and the Fisherfaces method.