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14
Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback
"... Abstract—The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback, which establishes a link between high-level concepts and low-level features, using the user’s feedback not only to assign proper weights to the features, but also to dynamically select them within ..."
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Cited by 13 (0 self)
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Abstract—The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback, which establishes a link between high-level concepts and low-level features, using the user’s feedback not only to assign proper weights to the features, but also to dynamically select them within a large collection of parameters. The target is to identify a set of relevant features according to a user query while at the same time maintaining a small sized feature vector to attain better matching and lower complexity. To this end, the image description is modified during each retrieval by removing the least significant features and better specifying the most significant ones. The feature adaptation is based on a hierarchical approach. The weights are then adjusted based on previously retrieved relevant and irrelevant images without further user-feedback. The algorithm is not fixed to a given feature set. It can be used with different hierarchical feature sets, provided that the hierarchical structure is defined a priori. Results achieved on different image databases and two completely different feature sets show that the proposed algorithm outperforms previously proposed methods. Further, it is experimentally demonstrated that it approaches the results obtained by state-of-the-art feature-selection techniques having complete knowledge of the data set. Index Terms—Adaptive retrieval, content-based image retrieval, relevance feedback. I.
Using high-level semantic features in video retrieval
- In Proc. of CIVR
, 2006
"... Abstract. Extraction and utilization of high-level semantic features are critical for more effective video retrieval. However, the performance of video retrieval hasn’t benefited much despite of the advances in high-level feature extraction. To make good use of high-level semantic features in video ..."
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Abstract. Extraction and utilization of high-level semantic features are critical for more effective video retrieval. However, the performance of video retrieval hasn’t benefited much despite of the advances in high-level feature extraction. To make good use of high-level semantic features in video retrieval, we present a method called pointwise mutual information weighted scheme(PMIWS). The method makes a good judgment of the relevance of all the semantic features to the queries, taking the characteristics of semantic features into account. The method can also be extended for the fusion of multi-modalities. Experiment results based on TRECVID2005 corpus demonstrate the effectiveness of the method. 1
H.H.: A corpus–based relevance feedback approach to cross–language image retrieval
- In: Proceedings of Cross Language Evaluation Forum (CLEF) 2005 Workshop
, 2005
"... Abstract. This paper regards images with captions as a cross-media parallel corpus, and presents a corpus-based relevance feedback approach to combine the results of visual and textual runs. Experimental results show that this approach performs well. Comparing with the mean average precision (MAP) o ..."
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Abstract. This paper regards images with captions as a cross-media parallel corpus, and presents a corpus-based relevance feedback approach to combine the results of visual and textual runs. Experimental results show that this approach performs well. Comparing with the mean average precision (MAP) of the initial visual retrieval, the MAP is increased from 8.29 % to 34.25 % after relevance feedback from cross-media parallel corpus. The MAP of cross-lingual image retrieval is increased from 23.99 % to 39.77 % if combining the results of textual run and visual run with relevance feedback. Besides, the monolingual experiments also show the consistent effects of this approach. The MAP of monolingual retrieval is improved from 39.52 % to 50.53 % when merging the results of the text and image queries. 1
Combining text and image queries at imageclef 2005
- In Working Notes of the CLEF. CLEF
, 2005
"... This paper presents our methods for the tasks of bilingual ad hoc retrieval and automatic annotation in ImageCLEF 2005. In ad hoc task, we propose a feedback method for cross-media translation in a visual run, and combine the results of visual and textual runs to generate the final result. Experimen ..."
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This paper presents our methods for the tasks of bilingual ad hoc retrieval and automatic annotation in ImageCLEF 2005. In ad hoc task, we propose a feedback method for cross-media translation in a visual run, and combine the results of visual and textual runs to generate the final result. Experimental results show that our feedback method performs well. Comparing to initial visual retrieval, average precision is increased from 8 % to 34 % after feedback. The performance is increased to 39 % if we combine the results of textual run and visual run with pseudo relevance feedback. In automatic annotation task, we propose several methods to measure the similarity between a test image and a category, and a test image is classified to the most similar
Y.C.: Language Translation and Media Transformation in CrossLanguage Image Retrieval
- ICADL 2006. LNCS
, 2006
"... Abstract. Cross-language image retrieval facilitates the use of text query in one language and image query in one medium to access image collection with text description in another language/medium. The images with annotations are considered as a trans-media parallel corpus. In a media-mapping approa ..."
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Cited by 1 (1 self)
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Abstract. Cross-language image retrieval facilitates the use of text query in one language and image query in one medium to access image collection with text description in another language/medium. The images with annotations are considered as a trans-media parallel corpus. In a media-mapping approach, we transform a query in one medium into a query in another medium by referencing to the aligned trans-media corpus. From the counterpart of results of an initial retrieval, we generate a new query in different medium. In the experiments, we adopted St. Andrews University Library’s photographic collection used in ImageCLEF, and explored different models of language translation and media transformation. When both text query and image query are given together, the best MAP of a cross-lingual cross-media model 1L2M (one language translation plus two media transformations) achieve 87.15 % and 72.39 % of those of mono-lingual image retrieval in the 2004 and the 2005 test sets, respectively. That demonstrates our media transformation is quite useful, and it can compensate for the errors introduced in language translation. 1
An Effective Mechanism to Neutralize the Semantic Gap in Content Based Image Retrieval (CBIR) IAJIT First Online Publication
, 2012
"... Abstract: Nowadays, Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features (such as shape, color etc.,) or high level features (human perception). Normall ..."
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Abstract: Nowadays, Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features (such as shape, color etc.,) or high level features (human perception). Normally, a semantic gap exists between the low level features and the high level features, because the images which are identical in visual content may not be identical in the semantic sense. In this paper, an effective approach is proposed to trim down this semantic gap that exists between the low level features and the high level features. Initially, when a query image is given, images relevant to it are retrieved from the image database based on its low level features. We have performed retrieval utilizing one of the evolutionary algorithms called Evolutionary Programming (EP). Subsequent to this process, query keyword which is a high level feature is extracted from these retrieved images and
A Hierarchical SVG Image Abstraction Layer for Medical Imaging
"... As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) ha ..."
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As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the “semantic gap”, a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible “layer ” that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.
Image Retrieval Using Visual Attention
, 2008
"... The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Content-based image retrieval systems are particularly vulnerable t ..."
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The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Content-based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work.
A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design.
An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.
