Content-based Image Recognition Research Articles

RETRACTED ARTICLE: Fuzzy Markov chain latent space probabilistic decision-making for feature optimization in content-based image recognition

RETRACTED ARTICLE: Fuzzy Markov chain latent space probabilistic decision-making for feature optimization in content-based image recognition

Content-Based Image Recognition and Tagging by Deep Learning Methods

Content-Based Image Recognition and Tagging by Deep Learning Methods

Research on Image Recognition Based on Neural Network

With the rapid development of Internet and mobile Internet, the image data in the network shows explosive growth. Image data is simple and intuitive, and contains rich information, which is widely used as a carrier of information exchange. Content-based image recognition can start from the image itself, first extract significant features from the image, and then recognize the image according to the differences of features, with good recognition effect. Neural network plays a very important role in artificial intelligence. Neural network is the key to realize artificial intelligence by learning data through algorithms and then making predictions and decisions. The image recognition of neural network is based on the image content. Neural network starts from the image data itself, extracts the image features of the bottom layer from the data, and then establishes the relationship between the image semantics of the bottom layer and the top layer through the algorithm of neural network, thus realizing the image recognition.

Research on Intelligent Recognition of Coast Image Features in Distributed System Based on Deep Learning

Xing, Z. and Li, G., 2020. Research on intelligent recognition of coast image features in distributed system based on deep learning. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 26–31. Coconut Creek (Florida), ISSN 0749-0208.The traditional content-based image recognition method has the disadvantages of low recognition efficiency and poor precision for coast images. Therefore, a massive image recognition method based on Hadoop is proposed in this paper, which implemented distributed computing for coast digital images. In this paper, the speeded up robust features (SURF) of images were acquired, and the SURF features of similar pictures were clustered via the k-means clustering algorithm. Finally, the image features were quantified by term frequency-inverse document frequency data mining technology, and the image was shaped according to the SURF features input by a user to achieve accurate identification of similar images. The analysis of results showed that the recognition means has superior efficiency and accuracy for identifying massive images, which greatly improved the recognition performance of the image and enhances the robustness of the system.

The Application of Content-based Image Emotional Semantic Recognition and Classification in Clothing E-commerce

People dress more and more sensual, more and more people like to wear clothing in line with their emotional expression, and online shopping convenience makes a lot of people like to buy clothing online, and the current clothing ecommerce websites do not classify clothing with their emotional semantic which could bring a certain inconvenience to the online buyers. Content-based image recognition and retrieval technology takes technical support for the emotional semantic automatic identification and classification of clothing commodity in the clothing e-commerce site. All the issues had been studied in this article which took clothing fabrics as an example to illustrate. All the work wants to cause the industry's attention.

Decision fusion-based approach for content-based image classification

PurposeCurrent practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.Design/methodology/approachThree novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.FindingsThe proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.Originality/valueTo the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

Content Based Image Recognition System using fused Image Color Histogram and Texture Features

Content Based Image Recognition System using fused Image Color Histogram and Texture Features

Novel feature extraction technique for content-based image recognition with query classification

A surge of concern has been witnessed to manage the growing size of image information available from various sources namely internet and digital image capturing devices. The rich content of information available with image data has proved to be useful for analytical decision-making process. Content-based image recognition has been considered as an effective measure to identify the object of interest. The success of aforementioned procedure has largely been influenced by the method of feature extraction from the image content. Image binarisation has proved to be an efficient tool for feature vector extraction using various threshold selection techniques. The authors have proposed a novel feature extraction technique based on local threshold selection and have evaluated the technique on 17,021 images for performance assessment. The precision results for classification and retrieval have shown an increment of 17% and 13.1% respectively when compared to state-of-the-art techniques. A statistical test has also been conducted to establish the significance of the proposed method over the existing techniques.

Novel feature extraction technique for content-based image recognition with query classification

Novel feature extraction technique for content-based image recognition with query classification

Retrieval Architecture with Classified Query for Content Based Image Recognition

The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene) dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

Accurate and efficient shape matching approach using vocabularies of multi-feature space representations

Selection of compressed, robust and accurate features is the fundamental ingredient of effective content-based image recognition and retrieval using shape information of objects in the image. In this paper, we present a four-stage system for real-time object recognition and retrieval that employs multiple feature space representation using contour information. In the first stage, we pre-process the shapes to cater for the presence of distortions such as cracks that can significantly distort the contour information of the shape. We then generate multiple feature space representations of shapes to be used later in proposed combination for efficient and accurate retrieval of shapes using a hierarchical indexing structure. To enable real-time image-based shape analysis by enhancing the efficiency and reducing the storage requirement of proposed shape descriptors, we present a quantization approach to generate vocabulary of feature space representation of shapes. These features are then combined in an ensemble for accurate and efficient shape retrieval and recognition in the presence of large shape datasets. The proposed system is evaluated using publicly available shape datasets such as MPEG 7, Swedish leaf and KIMIA 99 datasets. Our approach achieves higher accuracies which are better than state-of-the-art approaches reported in literature whilst looking at a small subset of shapes in dataset.

Content Based Image Recognition by Information Fusion with Multiview Features

Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

Sketch4Match: Content Based Image Recognition using Coloured Sketches

Sketch4Match: Content Based Image Recognition using Coloured Sketches

Recognition of 2-D ultrasound images of ovarian cysts using content-based image recognition (CBIR) system

OBJECTIVE: Pattern recognition of ultrasound abnormalities requires training and experience. To date, pattern recognition used by an expert remains the standardfor differentiating benign from malignant ovarian lesions. This pilot study was undertaken to develop and test the feasibility of a computer based system for recognition and classification of 2-D ultrasound images into categories.