Feature Selection for Image Retrieval based on Genetic Algorithm

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm

Automated Semantic Content Extraction from Images

In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image

Image Labeling and Classification by Semantic Tag Analysis

Image classification and retrieval plays a significant role in dealing with large multimedia data on the Internet. Social networks, image sharing websites and mobile application require categorizing multimedia items for more efficient search and storage. Therefore, image classification and retrieval methods gained a great importance for researchers and companies. Image classification can be performed in a supervised and semi-supervised manner and in order to categorize an unknown image, a statistical model created using pre-labeled samples is fed with the numerical representation of the visual features of images. A supervised approach requires a set of labeled data to create a statistical model, and subsequently classify an unlabeled test set. However, labeling images manually requires a great deal of time and effort. Therefore, a major research activity has gravitated to wards finding efficient methods to reduce the time and effort for image labeling. Most images on social websites have associated tags that somewhat describe their content. These tags may provide significant content descriptors if a semantic bridge can be established between image content and tags. In this thesis, we focus on cases where accurate class labels are scarce or even absent while some associated tags are only present. The goal is to analyze and utilize available tags to categorize database images to form a training dataset over which a dedicated classifier is trained and then used for image classification. Our framework contains a semantic text analysis tool based on WordNet to measure the semantic relatedness between the associated image tags and predefined class labels, and a novel method for labeling the corresponding images. The classifier is trained using only low-level visual image features. The experimental results using 7 classes from MirFlickr dataset demonstrate that semantically analyzing tags attached to images significantly improves the image classification accuracy by providing additional training data

Local selection of features and its applications to image search and annotation

In multimedia applications, direct representations of data objects typically involve hundreds or thousands of features. Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors. The neighborhood of the query object consists of those database objects that are close to the query object. The semantic quality of the neighborhood, which can be measured as the proportion of neighboring objects that share the same class label as the query object, is crucial for many applications, such as content-based image retrieval and automated image annotation. However, due to the existence of noisy or irrelevant features, errors introduced into similarity measurements are detrimental to the neighborhood quality of data objects. One way to alleviate the negative impact of noisy features is to use feature selection techniques in data preprocessing. From the original vector space, feature selection techniques select a subset of features, which can be used subsequently in supervised or unsupervised learning algorithms for better performance. However, their performance on improving the quality of data neighborhoods is rarely evaluated in the literature. In addition, most traditional feature selection techniques are global, in the sense that they compute a single set of features across the entire database. As a consequence, the possibility that the feature importance may vary across different data objects or classes of objects is neglected. To compute a better neighborhood structure for objects in high-dimensional feature spaces, this dissertation proposes several techniques for selecting features that are important to the local neighborhood of individual objects. These techniques are then applied to image applications such as content-based image retrieval and image label propagation. Firstly, an iterative K-NN graph construction method for image databases is proposed. A local variant of the Laplacian Score is designed for the selection of features for individual images. Noisy features are detected and sparsified iteratively from the original standardized feature vectors. This technique is incorporated into an approximate K-NN graph construction method so as to improve the semantic quality of the graph. Secondly, in a content-based image retrieval system, a generalized version of the Laplacian Score is used to compute different feature subspaces for images in the database. For online search, a query image is ranked in the feature spaces of database images. Those database images for which the query image is ranked highly are selected as the query results. Finally, a supervised method for the local selection of image features is proposed, for refining the similarity graph used in an image label propagation framework. By using only the selected features to compute the edges leading from labeled image nodes to unlabeled image nodes, better annotation accuracy can be achieved. Experimental results on several datasets are provided in this dissertation, to demonstrate the effectiveness of the proposed techniques for the local selection of features, and for the image applications under consideration

Feature selection for content-based image retrieval using statistical discriminant analysis

As we known, the very large repository of digital media arise the challenge of various digital search applications. In order to make use of this huge amount of data, effective tools are required for retrieve multimedia information. An image retrieval system is one of the tools that can be used for searching and retrieving images from a large database of digital images. However, there are several challenges and problems need to be considered when applied image retrieval system such as the gap between high-level semantic concept and low-level visual features. This refers to problem of feature selection, which is critical to really solve the gap problem in CBIR. Recently, the most feasible feature selection method is discriminant analysis. Therefore, in this project, we proposed title feature selection in content-based image retrieval using statistical discriminant analysis. In the project, we intended to enhance performance by improve the feature selection process. Besides, we used fuzzy theory in content-based image retrieval to solve the problem of perspective subjectivity of human in image retrieval. The system would be more depends to the human-like and how to response with relevant images that match the concept of current query is always the research question in this project

Evaluation of fuzzy rough set feature selection for content based image retrieval system with noisy images

In this paper Fuzzy Rough Set is used for feature selection in the Content Based Image Retrieval system. Noisy query images are fed to this Content Based Image Retrieval system and the results are compared with four other feature selection methods. The four other feature selection methods are Genetic Algorithm, Information Gain, OneR and Principle Component Analysis. The main objective of this paper is to evaluate the rules which are extracted from fuzzy rough set and determine whether these rules which are used for training the Support Vector Machine can deal with noisy query images as well as the original queried images. To evaluate the Fuzzy Rough set feature selection, we use 10 sematic group images from COREL database which we have purposely placed some defect by adding Gaussian, Poisson and Salt and Pepper noises of different magnitudes. As a result, the proposed method performed better in term of accuracies in most of the different types of noise when compared to the other four feature selection methods

Exploring features for complicated objects: Cross-view feature selection for multi-instance learning

Copyright 2014 ACM. In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multiview representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation

Feature Selection for Image Retrieval based on Genetic Algorithm

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm