This paper describes the participation of Tel Aviv University Medical Image Processing Laboratory group at the ImageClef 2008 medical retrieval and medical annotation tasks. In both tasks we have used the bag-of-words approach for image representation. We submitted two purely visual automatic runs to the medical retrieval task, which used different normalization in the feature extraction stage....

Content-based medical image retrieval is an important tool for doctors in their daily activity. In this paper, we propose a novel image retrieval framework to combine visual concept and local features. To obtain visual semantic representation of the image, we first construct a graph model by feature distance and density similarity, and then a graph-based semi-supervised learning method is appli...

This paper presents an approach for 3D object retrieval, dedicated to partial shape retrieval in large datasets. A Bag-of-Words representation is employed, based on the extraction of 3D Harris points and on a local description involving local Fourier descriptors. By adding ∆-TSR, a triangular spatial information between words, the richness and robustness of this representation is reinforced. Th...

One of the biggest challenges in content based image retrieval is to solve the problem of “semantic gaps” between low-level features and high-level semantic concepts. In this paper, we aim to investigate various combinations of mid-level features to build an effective image retrieval system based on the bag-offeatures (BoF) model. Specifically, we study two ways of integrating the SIFT and LBP ...

This paper evaluates the retrieval effectiveness degradation when facing with noisy text corpus. With the use of a test-collection having the clean text, another version with around 5% error rate in recognition and a third with 20% error rate, we have evaluated six IR models based on three text representations (bag-of-words, n-grams, trunc-n) as well as three stemmers. Using the mean reciprocal...

Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. In this study, a content-based image retrieval system that is based on scene classification for image indexing is proposed. Instead of using low-level features directly, semantic class information that is obtained as a result of scene classification is use...

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology...

In this paper a supervised codebook learning technique for the Bag-of-Features representation that optimizes the learned codebooks towards face retrieval is proposed. This allows to use significantly smaller codebooks reducing both the storage requirements and the retrieval time allowing the proposed technique to efficiently scale to large datasets. The proposed method is also combined with a s...

The Problem: Traditional information retrieval systems based on the “bag-of-words” paradigm cannot capture the semantic content of documents. While these systems are relatively robust and have high recall, they suffer from very poor precision. On the other hand, it is impossible with current technology to build a practical information access system that fully analyzes and understands unrestrict...

In this paper a novel object signature is proposed for 3D object retrieval and partial matching. A part-based representation is obtained by partitioning the objects into subparts and by characterizing each segment with different geometric descriptors. Therefore, a Bag of Words framework is introduced by clustering properly such descriptors in order to define the so called 3D visual vocabulary. ...