CBIR Approach Research Articles

Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm- Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

Multi-level colored directional motif histograms for content-based image retrieval

Color features and local geometrical structures are the two basic image features which are sufficient to convey the image semantics. Both of these features show diverse nature on the different regions of a natural image. Traditional local motif patterns are standard tools to emphasize these local visual image features. These motif-based schemes consider either structural orientations or limited directional patterns which are not sufficient to realize the detailed local geometrical properties of an image. To address these issues, we have proposed a new multi-level colored directional motif histogram (MLCDMH) for devising a content-based image retrieval scheme. The proposed scheme extracts local structural features at three different levels. Initially, MLCDMH scheme extracts directional structural patterns from a $$3 \times 3$$ pixel grids of an image. This reflects the $$9^9$$ different structural arrangements using 28 directional patterns. Next, we have used a weighted neighboring similarity (WNS) scheme to exploit the uniqueness of each motif pixel in its local surrounding. The WNS scheme will compute the importance of each directional motif pattern in its $$3 \times 3$$ local neighborhood. In the last level, we have fused all directional motif images into a single directional difference matrix which reflects the local structural and directional motif features in detail and also reduces the computation overhead. The MLCDMH considers all possible permutations and rotations of the motif patterns to generate rotational invariant structural features. The image retrieval performance of this proposed scheme has been evaluated using different Corel/natural, object, texture and heterogeneous image datasets. The results of the retrieval experiments have shown satisfactory improvement over other motif- and non-motif-based CBIR approaches.

Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs

BackgroundThe majority of current medical CBIR systems perform retrieval based only on “imaging signatures” generated by extracting pixel-level quantitative features, and only rarely has a feedback mechanism been incorporated to improve retrieval performance. In addition, current medical CBIR approaches do not routinely incorporate semantic terms that model the user’s high-level expectations, and this can limit CBIR performance. MethodWe propose a retrieval framework that exploits a hybrid feature space (HFS) that is built by integrating low-level image features and high-level semantic terms, through rounds of relevance feedback (RF) and performs similarity-based retrieval to support semi-automatic image interpretation. The novelty of the proposed system is that it can impute the semantic features of the query image by reformulating the query vector representation in the HFS via user feedback. We implemented our framework as a prototype that performs the retrieval over a database of 811 radiographic images that contains 69 unique types of bone tumors. ResultsWe evaluated the system performance by conducting independent reading sessions with two subspecialist musculoskeletal radiologists. For the test set, the proposed retrieval system at fourth RF iteration of the sessions conducted with both the radiologists achieved mean average precision (MAP) value ∼0.90 where the initial MAP with baseline CBIR was 0.20. In addition, we also achieved high prediction accuracy (>0.8) for the majority of the semantic features automatically predicted by the system. ConclusionOur proposed framework addresses some limitations of existing CBIR systems by incorporating user feedback and simultaneously predicting the semantic features of the query image. This obviates the need for the user to provide those terms and makes CBIR search more efficient for inexperience users/trainees. Encouraging results achieved in the current study highlight possible new directions in radiological image interpretation employing semantic CBIR combined with relevance feedback of visual similarity.

An end to end secure CBIR over encrypted medical database.

In this paper, we propose a new secure content based image retrieval (SCBIR) system adapted to the cloud framework. This solution allows a physician to retrieve images of similar content within an outsourced and encrypted image database, without decrypting them. Contrarily to actual CBIR approaches in the encrypted domain, the originality of the proposed scheme stands on the fact that the features extracted from the encrypted images are themselves encrypted. This is achieved by means of homomorphic encryption and two non-colluding servers, we however both consider as honest but curious. In that way an end to end secure CBIR process is ensured. Experimental results carried out on a diabetic retinopathy database encrypted with the Paillier cryptosystem indicate that our SCBIR achieves retrieval performance as good as if images were processed in their non-encrypted form.

A Hybrid Similarity Measure for Mammography CAD Using CBIR Approach

A Hybrid Similarity Measure for Mammography CAD Using CBIR Approach

An Identification of the Image Retrieval Domain from the Perspective of Library and Information Science with Author Co-citation and Author Bibliographic Coupling Analyses

As the improvement of digital technologies increases the use of images from various fields, the domain of image retrieval has evolved and become a growing topic of research in the Library and Information Science field. The purpose of this study is to identify the knowledge structure of the image retrieval domain by using the author co-citation analysis and author bibliographic coupling as analytical tools in order to understand the domain’s past and present. The data set for this study is 245 articles with 8,031 cited articles in the field of image retrieval from 1998 to 2013, from the Web of Science citation database. According to the results of author co-citation analysis for the past of the image retrieval domain, our findings demonstrate that the intellectual structure of image retrieval in the LIS field consists of predominantly user-oriented approaches, but also includes some areas influenced by the CBIR area. More specifically, the user-oriented approach contains six specific areas which include image needs, information seeking, image needs and search behavior, image indexing and access, indexing of image collection, and web image search. On the other hand, for CBIR approaches, it contains feature-based image indexing, shape-based indexing, and IR & CBIR. The recent trends of image retrieval based on the results from author bibliographic coupling analysis show that the domain is expanding to emerging areas of medical images, multimedia, ontology- and tag-based indexing which thus reflects a new paradigm of information environment.

A Collaborative Approach to Enhance CBIR Performance using DCT, DST and Kekre's Transform

Content based Image retrieval is a process which will enhance the image searching accuracy. Many researchers are working on image processing. CBIR approach uses different techniques such as colour, texture and shape, by processing this feature vector is generated and comparison is done. CBIR approach can be used in many places such as search engines, patent registration, face detection etc. CBIR approach can also be used for security. It can encrypt and decrypt the image content to provide security. Stenography along with CBIR can generate algorithm which makes data more secure. CBIR can help in many fields only by referring image content. In this paper new approach “Hybrid approach” is implemented. Hybrid approach is a combination of different transforms. Combination of DCT, DST and kekre’s transforms are used for feature vector generation. For image matching and distance calculation, two methods are used in this paper known as Euclidian distance and absolute distance method. In this paper, different transforms are combined to generate a hybrid approach. Results of different hybrid approaches are compared in this paper. It includes comparison of all the algorithms based on their performance by comparing different performance parameters such as precision and recall to determine which algorithm is providing the best result.

A Novel Approach of Color-Texture based CBIR Using Fuzzy Logic

Image retrieval is an emerging research area for multimedia database. It is the basic requirement task in the present scenario. In this paper we propose a novel CBIR approach using Fuzzy clustering. The proposed method firstly clusters the images based on RGB components, using Fuzzy clustering algorithm, secondly the image retrieval is performed based on texture element in the clustered database. The performance of the system is evaluated on the most common evaluation method namely Precision and Recall method. The proposed method achieves higher efficiency than the texture based retrieval techniques.

A Decision Making Optimization Method for Mammography CAD using CBIR Approach

A Decision Making Optimization Method for Mammography CAD using CBIR Approach

An Efficient CBIR Approach for Diagnosing the Stages of Breast Cancer Using KNN Classifier

This paper proposes a mammogram image retrieval technique using pattern similarity scheme. Comparing previous and current mammogram images associated with pathologic conditions are used to diagnose the real stage of breast cancer by doctors. Lack of awareness and screening programs causes the breast cancer deaths. Early detection is the best way to reduce the deaths per incident ratio. Mammogram is the best one in the currently used technique for diagnosing breast cancer. In this paper, the retrieval process is divided into four distinct parts that are feature extraction, kNN classification, pattern instantiation and computation of pattern similarity. In feature extraction step, low level texture features like entropy, homogeneity, contrast, energy, correlation and run length matrix features are extracted. These extracted features are classified using K- Nearest Neighbor classifier to differentiate the normal tissue from abnormal one. Each group is considered as patterns. Finally, pattern similarity is estimated for retrieving images based on their similarity with the query image. This scheme is effectively applied to the Content Based Image Retrieval systems to retrieve the images from large databases and identify the real stage of breast cancer. If we find cancer in early stages we can cure it.