High-dimensional visual vocabularies for image retrieval

Magalhães, João; Rueger, Stefan

doi:10.1145/1277741.1277923

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Clustering techniques are one of the most used space partitioning techniques, with applications that range from image retrieval [26] to image indexing [18]. e search space is partitioned by generating a set of centroids, and vectors are assigned to the closest centroid according to a metric (e.g.…”

Section: Related Workmentioning

confidence: 99%

Balanced Search Space Partitioning for Distributed Media Redundant Indexing

Mourão

Magalhães

2017

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

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is paper addresses the problem of balanced, redundant indexing of media information. Our goal is to partition and distribute the search index, taking advantage of the distributed systems properties: balanced load across nodes, redundancy on node down and e cient node usage under concurrent querying. We follow an information compression approach to solve this problem and propose to represent data with overcomplete codebooks, where each document is represented by only a few codewords and an indexing node is responsible for several codewords.antization algorithms are designed to t the original data as best as possible, leading to bias towards codewords that t the principal directions of data. In this paper, we propose the balanced KSVD (B-KSVD) algorithm, that distributes the allocation of data across a balanced number of codewords, according to the global distribution of data. Indexing experiments showed that B-KSVD can achieve 38% 1-recall by inspecting only 1% of the full index, distributed over 10 partitions. Traditional methods based on k-means need to either use larger codebooks or to inspect a larger portion of the index to achieve the same retrieval performance.

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Section: Related Workmentioning

confidence: 99%

Balanced Search Space Partitioning for Distributed Media Redundant Indexing

Mourão

Magalhães

2017

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

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“…We choose the multinomial distribution, as the binomial distribution is too limiting given the probabilistic nature of our problem. The description of the naïve Bayes implementation used in our experiments is in [24].…”

Section: Baseline Naïve Bayes Modelmentioning

confidence: 99%

Information-theoretic semantic multimedia indexing

Magalhães

Rüger

2007

Proceedings of the 6th ACM International Conference on Image and Video Retrieval

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To solve the problem of indexing collections with diverse text documents, image documents, or documents with both text and images, one needs to develop a model that supports heterogeneous types of documents. In this paper, we show how information theory supplies us with the tools necessary to develop a unique model for text, image, and text/image retrieval. In our approach, for each possible query keyword we estimate a maximum entropy model based on exclusively continuous features that were preprocessed. The unique continuous feature-space of text and visual data is constructed by using a minimum description length criterion to find the optimal feature-space representation (optimal from an information theory point of view). We evaluate our approach in three experiments: only text retrieval, only image retrieval, and text combined with image retrieval.

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“…Another approach is to create a high-dimensional visual vocabulary that represents the image documents [15]. Even if these methods are applied, however, a problem remains: it is difficult to index images using abstract terms simply by analyzing the image contents.…”

Section: Related Workmentioning

confidence: 99%

Can Social Tagging Improve Web Image Search?

Katô

Ohshima

Oyama

et al.

Lecture Notes in Computer Science

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Abstract. Conventional Web image search engines can return reasonably accurate results for queries containing concrete terms, but the results are less accurate for queries containing only abstract terms, such as "spring" or "peace." To improve the recall ratio without drastically degrading the precision ratio, we developed a method that replaces an abstract query term given by a user with a set of concrete terms and that uses these terms in queries input into Web image search engines. Concrete terms are found for a given abstract term by making use of social tagging information extracted from a social photo sharing system, such as Flickr. This information is rich in user impressions about the objects in the images. The extraction and replacement are done by (1) collecting social tags that include the abstract term, (2) clustering the tags in accordance with the term co-occurrence of images, (3) selecting concrete terms from the clusters by using WordNet, and (4) identifying sets of concrete terms that are associated with the target abstract term by using a technique for association rule mining. Experimental results show that our method improves the recall ratio of Web image searches.

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High-dimensional visual vocabularies for image retrieval

Cited by 8 publications

References 2 publications

Balanced Search Space Partitioning for Distributed Media Redundant Indexing

Balanced Search Space Partitioning for Distributed Media Redundant Indexing

Information-theoretic semantic multimedia indexing

Can Social Tagging Improve Web Image Search?

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