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Improving image retrieval effectiveness via multiple queries
- in First ACM International Workshop on Multimedia Databases (MMDB’03
, 2003
"... Abstract. Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Altho ..."
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Cited by 17 (3 self)
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Abstract. Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may utilize the information contained in multiple queries (gotten in one step or through a feedback process), this is often only a reformulation of the original query. As a result most of these strategies only get the images in some neighborhood of the original query as the retrieval result. This severely restricts the system performance. Relevance feedback techniques are generally used to mitigate this problem. In this paper, we present a novel approach to relevance feedback which can return semantically related images in different visual clusters by merging the result sets of multiple queries. We also provide experimental results to demonstrate the effectiveness of our approach. Keywords: content-based image retrieval, multi-channel CBIR, result merging, relevance feedback
Toward cross-language and cross-media image retrieval
- Proceedings of the 5 th Workshop of the Cross-Language Evaluation Forum. CLEF 2004, Springer: Proceedings Multilingual Information Access for Text, Speech and Images
, 2004
"... This report describes the approach used in our participation of ImageCLEF. Our focus is on image retrieval using text, i.e. Cross-Media IR. To do this, we first determine the strong relationships between keywords and types of visual features. Then the subset of images retrieved by text retrieval is ..."
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Cited by 10 (1 self)
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This report describes the approach used in our participation of ImageCLEF. Our focus is on image retrieval using text, i.e. Cross-Media IR. To do this, we first determine the strong relationships between keywords and types of visual features. Then the subset of images retrieved by text retrieval is used as examples to match other images according to the most important types of features of the query words. 1
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2011
"... Abstract—Increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level imag ..."
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Cited by 8 (0 self)
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Abstract—Increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user provided tags are diverse which means that TIAM is very sparse thus making it difficult to reliably estimate tag to tag co-occurrence probabilities. We developed a collaborative filtering method based on non-negative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.
Contents lists available at ScienceDirect Information Sciences
"... journal homepage: www.elsevier.com/locate/ins Image retrieval model based on weighted visual features ..."
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journal homepage: www.elsevier.com/locate/ins Image retrieval model based on weighted visual features
