Improving Retrieval Performance by Region Constraints and Relevance Feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.

Online Metric Learning for Relevance Feedback in E-Commerce Image Retrieval Applications

Relevance feedback plays a key role in multiple feature-based image retrieval applications. This paper describes an online metric learning approach for a set of ranking functions. In the feedback round, the most relevant and most nonrelevant images related to the target image are selected to construct a relative comparison triplet. The weighting parameters of the multiple ranking functions are updated by minimizing a quadratic objective function constrained by the triplet. The approach unifies the learning algorithm for the most commonly used ranking functions. Thus, multiple features with their own ranking function can easily be employed in the ranking module without feature reconstruction. The method is computationally inexpensive and appropriate for large-scale e-commerce image retrieval applications. Customized ranking functions are well supported. Practically, simplified ranking functions yield better results when the number of query rounds is relatively small. Experiments with an image dataset from a real e-commerce platform show the superiority of the proposed approach.

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentially.Processing the variety of remotely sensed data has increasingly been complex and difficult.It is also hard to efficiently and intelligently retrieve what users need from a massive database of images.This paper introduces an improved support vector machine(SVM)model,which optimizes the model parameters and selects the feature subset based on the particle swarm optimization(PSO)method and genetic algorithm(GA)for remote sensing image retrieval.The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID,PSO,and GA in terms of consistency and stability.

Mass detection algorithm based on support vector machine and relevance feedback

To improve the detection of mass with appearance that borders on the similarity between mass and density tissues in the breast,an support vector machine classifier based on typical features is designed to classify the region of interest(ROI).Furthermore,relevance feedback is introduced to improve the performance of support vector machines.A new mass detection scheme based on the support vector machine and the relevance feedback is proposed.Simulation experiments on mammograms illustrate that the novel support vector machine classifier based on typical features can improve the detection performance of the featureless classifier by 5%,while the introduction of relevance feedback can further improve the detection performance to about 90%.

Query Expansion Based on Semantics and Statistics in Chinese Question Answering System

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.

Audio Segmentation via the Similarity Measure of Audio Feature Vectors

A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to [0,1）. To fulfill the task of audio segmentation, a self-similarity matrix is computed to reveal the inner structure of an audio clip to be segmented. As the final result must be consistent with the subjective evaluation and be adaptive to some special applications, a set of weights is adopted, which can be modified through relevance feedback techniques. Experiments show that satisfactory result can be achieved via the algorithm proposed in this paper.

HBIR: Hypercube-Based Image Retrieval

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval （CBIR） systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube （HC） in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the effective rules and suppress the inefficient ones. For improving the response time, an HC merging approach and, for reducing the conflicts, an HC splitting method is designed. Our experiments on a set of 11 000 images from the Corel database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to some existing approaches reported recently in the literature. Moreover, our approach can be better trained and is not saturated in long time, i.e., any feedback improves the precision and recall of the system. Another strength of our method is its ability to address the dynamic nature of the image database such that it can follow the changes occurred instantaneously and permanently by adding and dropping images.