An Image Clustering and Feedback-Based Retrieval Framework

The research from cognitive psychology and neurobiology suggests that people have a strong ability to perceive objects before identifying them, and the human vision system (HVS) processes some salient regions of an image while leaving others nearly unprocessed. Based on this observation, we assume people prefer to pay more attention to salient regions when judging whether images are matched. A novel saliency detection based on reconstruction residual is proposed to calculate saliency maps and generate salient regions, which helps decrease the interference from irrelevant regions in image retrieval. The introduction of integrated region matching (IRM) can better characterize the spatial constraints among salient regions to conduct similarity measurement. The experimental results demonstrate that the proposed saliency detection has a good performance. At the same time, the combination of salient regions and IRM can help improve the state-of-the-art retrieval algorithms.

Although relevance feedback (RF) has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF because of scalability, efficiency, and effectiveness issues. In this paper, we propose a unified relevance feedback framework for Web image retrieval. Our framework shows advantage over traditional RF mechanisms in the following three aspects. First, during the RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Second, the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Thus, we could integrate RF into Web image retrieval in a practical way. Last, a new user interface (UI) is proposed to support implicit RF. On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users' click-through data as implicit relevance feedback in order to release burden from the users. On the other hand, unlike traditional RF UI which hardily substitutes subsequent results for previous ones, a recommendation scheme is used to help the users better understand the feedback process and to mitigate the possible waiting caused by RF. Experimental results on a database consisting of nearly three million Web images show that the proposed framework is wieldy, scalable, and effective.

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel object-based image retrieval framework that integrates effective pre-treatment and re-ranking is presented, (2) a new feature filtration method based on attention analysis is proposed for pre-treatment, (3) to further improve object retrieval precision, we add an efficient spatial configuration model to re-rank the primary retrieval result using Bag of Word method. Experimental results demonstrate the effectiveness of our method.

The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead but also cannot well protect the image and query privacy in multi-user scenarios. To solve the above problems, we first propose a basic privacy-preserving content-based image retrieval (CBIR) framework which significantly reduces storage and communication overhead compared with the previous works. Furthermore, we design a new efficient key conversion protocol to support unshared key multi-owner multi-user image retrieval without losing search precision. Moreover, our framework supports unbounded attributes and can trace malicious users according to leaked secret keys, which significantly improve the usability of multi-source data sharing. Strict security analysis shows that the user privacy and outsourced data security can be guaranteed during the image retrieval process, and the performance analysis using real-world dataset shows that the proposed image retrieval framework is efficient and feasible for practical applications.

The vast growth of Internet and digital contents of Internet creates much interest on automated image indexing and retrieval techniques. Most commercial Content Based Image Retrieval systems use color histograms as one of the features. In this work, we have prepared combined color histograms for three regions (top, bottom and middle) of the image. The combination of those histograms will indirectly include the space information within it. Besides this, straight-line signatures and outline sketch signatures from the image are extracted. The average precision is used as a metric to evaluate the performance of the system. Experiments were successfully implemented on 1000 images of SIMPLIcity database using MAT Lab. The performance of our method is compared with four other previous methods Integrated Region Matching[l], Fuzzy Integrated Region Matching [2], Color Salient Points and shape features [3] and Interactive CBIR using Ripplet Transform and Fuzzy Relevance Feedback Mechanism[4] which used the same data set and evaluation method. According to the arrived results, the performance of the proposed Three Region Color, Straight-Line and Outline Sketch Signatures based CBIR system was significantly better than other compared methods.

To overcome the semantic gap in the retrieval process, the user relevance feedback frame work is added to the current image retrieval system. Relevance feedback method iteratively refines and updates the retrieved result by learning the user-labeled examples to further improves the overall performance. In the proposed scheme feature descriptor derived from the EDBTC compressed data stream. Firstly, a color image is decomposed using EDBTC scheme to produce two new image representations, namely color quantizer and bitmap image by using Improved LBG-VQ (ILBG)algorithm. Two image feature descriptors called Color Histogram Feature (CHF), and Bit Pattern Feature(BPF) can be subsequently generated from the EDBTC color quantizer and its corresponding bitmap image respectively without performing the decoding process. The similarity degree between two images is simply measured with the similarity distance score of their feature descriptor. Second, we presented a relevance feedback (RF) framework for effective image retrieval by using a support vector machine (SVM). Effectiveness of EDBTC feature descriptor is quantitatively examined and compared in the RGB color space as well as in HSI color channel. Extensive experiments shows that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF.

Recent years, the stereo image has become an emerging media in the field of 3D technology, which leads to an urgent demand of stereo image retrieval. In this paper, we attempt to introduce a framework for object-based stereo image retrieval (OBSIR), which retrieves images containing the similar objects to the one captured in the query image by the user. The proposed approach consists of both online and offline procedures. In the offline procedure, we propose a salient object segmentation method making use of both color and depth to extract objects from each image. The extracted objects are then represented by multiple visual feature descriptors. In order to improve the image search efficiently, we construct an approximate nearest neighbor (ANN) index using cluster-based locality sensitive hashing (LSH). In the online stage, the user may supply the query object by selecting a region of interest (ROI) in the query image, or clicking one of the objects recommended by the salient object detector. For the image retrieval evaluation we build a new dataset containing over 10K stereo images. The experiments on this dataset show that the proposed method can effectively recommend the correct object and the final retrieval result is also better than other baseline methods.

We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems infeasible, we are able to approximate this density by exploiting statistics of the image database domain. Unlike previous approaches that assume an arbitrary distribution for the unconditional density of the feature vector (the density of the features taken over the entire image domain), we learn both the structure and the parameters of this density. These density estimates enable us to construct a Bayesian classifier. Using this Bayesian classifier, we perform a windowed scan over images for objects of interest and employ the user's feedback on the search results to train a second classifier that focuses on eliminating difficult false positives. We have incorporated this algorithm into an object-based image retrieval system. We demonstrate the effectiveness of our approach with experiments using a set of categories from the Corel database.

In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

Feature extraction and similarity measure are two basic key issues in image retrieval. Combining the advantages of SNN in image recognition and selective attention for image retrieval, a novel visual keywords-driven image retrieval approach based on these properties has been proposed. By using a predefined set of visual keywords as prototype patterns stored with the SNN and then measuring the degree of similiarity of the stored images to the visual keywords, we show that such a visual keyword driven SNN can provide the framework for image indexing or retrieval which is scalable, robust and efficient for web-based search.

The objective for this paper is to develop an adaptive image retrieval system with an innovative approach to use artificial neural networks and clustering techniques to retrieve images similar to the input image. This paper involves retrieving images from huge image databases which are visually similar to a query image. Due to the enormous increase in image database size, and its high usage in a variety of applications, need for the development of Content Based Image Retrieval arose. It summarizes the problem, the proposed solution, and the desired results. The system uses neural networks, relevance feedback and clustering. The system is made intelligent by making the system learn the user's preference as feedback. The relevance feedback is used to improve the precision of the system by analyzing user's relevance feedback for each retrieved image while neural network and clustering techniques are used to reduce the time complexity of the system. The system uses three-layered neural network to train the system using image clusters as training dataset by a supervised approach. Also after taking the feedback from the user, the image clusters are re-clustered by rearranging the images after a successful retrieval. Given a user query as an image, the neural system retrieves related images by computing similarities with images in the given image clusters. To provide preference, from all the retrieved images user selects an image as relevant one and all other are hence treated as irrelevant ones. So, the rank of the selected image is increased while the ranks of other images are decreased. With this feedback, the system refinement method estimates global approximations and adjusts the similarity probabilities.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

In this paper, we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.

Image Indexing and Retrieval Using Object-based Point Feature Maps

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.