Incorporating multiple SVMs for automatic image annotation

Qi, Xiaojun; Han, Yutao

doi:10.1016/j.patcog.2006.04.042

Cited by 151 publications

(71 citation statements)

References 16 publications

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“…This choice is also supported by the findings in image annotation [12], where the block-based scheme has been proven to achieve comparable annotation results as the complicated image segmentation scheme. In our system, we divide each image into 9 blocks with the same size as illustrated in Fig.…”

Section: Low-level Visual Features and Their Distance Measurementioning

confidence: 71%

Block-based long-term content-based image retrieval using multiple features

Xiao

2013

2013 IEEE International Conference on Multimedia and Expo (ICME)

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This paper proposes a novel content-based image retrieval technique, which integrates block-based visual features and user's query concept-based semantic features. It also facilitates short-term and long-term learning processes by integrating users' historical relevance feedback information. The history is compactly stored in a semantic feature matrix and efficiently represented as semantic features of the images. The short-term relevance feedback technique can benefit from long-term learning. The high-level semantic features are dynamically updated based on users' query concept and therefore represent the image's semantic meaning more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space.Index Terms-Content-based image retrieval, relevance feedback, semantic feature matrix

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Section: Low-level Visual Features and Their Distance Measurementioning

confidence: 71%

Block-based long-term content-based image retrieval using multiple features

Xiao

2013

2013 IEEE International Conference on Multimedia and Expo (ICME)

Self Cite

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“…They used different sets of classifiers, estimated the decision for each set using majority voting and finally fused all decisions to get the final label. Qj et al [24] have also used a three level classifier for two sets of SVM classifiers. The first group uses global features and the second group employs local features.…”

Section: Previous Workmentioning

confidence: 99%

ImageCLEF 2014: Overview and Analysis of the Results

Caputo

Müller

Martínez‐Gómez

et al. 2014

Lecture Notes in Computer Science

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Abstract. This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.

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“…The hierarchical organization of WordNet leads to the concept of implication / likelihood among words. In [16], an automatic image annotation system is proposed, which integrates two sets of Support Vector Machines (SVMs), namely the Multiple Instance Learning (MIL)-based (applied to image blocks) and global feature-based SVMs (using global color and texture features), for annotation. Finally, an approach for automatic annotation propagation in 3D object databases is proposed in [17], based on propagation of probabilities through neuro-fuzzy controllers, using a combination of low-level geometric and high-level semantic information.…”

Section: Background and Related Workmentioning

confidence: 99%

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Lazaridis

Αξενόπουλος

Rafailidis

et al. 2013

Signal Processing: Image Communication

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In this paper, a novel framework for multimodal search and retrieval of rich media objects is presented. The searchable items are media representations consisting of multiple modalities, such as 2D images, 3D objects and audio files, which share a common semantic concept. A manifold learning technique based on Laplacian Eigenmaps was appropriately modified in order to merge the low-level descriptors of each separate modality and create a new low-dimensional multimodal feature space, where all media objects can be mapped irrespective of their constituting modalities. To accelerate search and retrieval and make the framework suitable even for web-scale applications, a multimedia indexing scheme is adopted, which represents each object of the dataset by the ordering of a number of reference objects. Moreover, the hubness property is introduced in this paper as a criterion to select the most representative reference objects, thus, achieving the maximum possible performance of indexing. The content-based similarity of the multimodal descriptors is also used to automatically annotate the objects of the dataset using a predefined set of attributes. Annotation propagation is utilized to approximate the multimodal descriptors for multimodal queries that do not belong to the dataset.

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Incorporating multiple SVMs for automatic image annotation

Cited by 151 publications

References 16 publications

Block-based long-term content-based image retrieval using multiple features

Block-based long-term content-based image retrieval using multiple features

ImageCLEF 2014: Overview and Analysis of the Results

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

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