Discriminative Feature Selection in Image Classification and Retrieval

2012 ◽  
pp. 216-230
Author(s):  
Shang Liu ◽  
Xiao Bai
Keyword(s):  
Image Classification ◽  
Bag Of Words ◽  
Current Image ◽  
Visual Words ◽  
Discriminative Feature ◽  
Label Information ◽  
Novel Method ◽  
Category Knowledge ◽  
Supervised Image Classification ◽  
High Level

In this chapter, the authors present a new method to improve the performance of current bag-of-words based image classification process. After feature extraction, they introduce a pairwise image matching scheme to select the discriminative features. Only the label information from the training-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images, and thus highlight the high level category knowledge of images. Visual words are constructed on these selected features. This novel method could be used as a refinement step for current image classification and retrieval process. The authors prove the efficiency of their method in three tasks: supervised image classification, semi-supervised image classification, and image retrieval.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

2018 ◽  
Vol 45 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Amna Sarwar ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Khurram Ashfaq Qazi ◽  
Ahmed Adnan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Image Databases ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Bag Of Words ◽  
Visual Words ◽  
Visual Attributes ◽  
Visual Vocabulary ◽  
Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

An Optimized Method for Image Classification Based on Bag of Words Model

2013 ◽  
Vol 34 (9) ◽  
pp. 2064-2070 ◽  
Author(s):  
Chun-hui Zhao ◽  
Ying Wang ◽  
KANEKO Masahide
Keyword(s):  
Image Classification ◽  
Bag Of Words

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Analysis of ancient DNA from a prehistoric Amerindian cemetery

1999 ◽  
Vol 354 (1379) ◽  
pp. 153-159 ◽  
Author(s):  
Anne C. Stone ◽  
Mark Stoneking
Keyword(s):  
Ancient Dna ◽  
Dna Analysis ◽  
Nuclear Dna ◽  
Genetic Research ◽  
Hypervariable Region ◽  
Dna Preservation ◽  
European Contact ◽  
Novel Method ◽  
High Level ◽  
Central Illinois

The Norris Farms No. 36 cemetery in central Illinois has been the subject of considerable archaeological and genetic research. Both mitochondrial DNA (mtDNA) and nuclear DNA have been examined in this 700–year–old population. DNA preservation at the site was good, with about 70% of the samples producing mtDNA results and approximately 15% yielding nuclear DNA data. All four of the major Amerindian mtDNA haplogroups were found, in addition to a fifth haplogroup. Sequences of the first hypervariable region of the mtDNA control region revealed a high level of diversity in the Norris Farms population and confirmed that the fifth haplogroup associates with Mongolian sequences and hence is probably authentic. Other than a possible reduction in the number of rare mtDNA lineages in many populations, it does not appear as if European contact significantly altered patterns of Amerindian mtDNA variation, despite the large decrease in population size that occurred. For nuclear DNA analysis, a novel method for DNA–based sex identification that uses nucleotide differences between the X and Y copies of the amelogenin gene was developed and applied successfully in approximately 20 individuals. Despite the well–known problems of poor DNA preservation and the ever–present possibility of contamination with modern DNA, genetic analysis of the Norris Farms No. 36 population demonstrates that ancient DNA can be a fruitful source of new insights into prehistoric populations.

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