Hashing based re-ranking of web images using query-specific semantic signatures

Jalindar, Dange Bapusaheb; Kshirsagar, Deepak

doi:10.1109/icisim.2017.8122168

Cited by 5 publications

(1 citation statement)

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“…These likenesses are figured by looking at separation between the inquiry image and images in the pool. Further only the images retrieved in pool of images are compared with offline stored features as semantic signature [24] for finding the similar images not the entire features of images to reduce computational complexity. We used distance formula given in (1) for semantic signature matching and Cityblock distance for texture features to obtain similarity between images to rerank given by…”

Section: Feature Extractionmentioning

confidence: 99%

Capturing User Intention to Enhance Image Retrieval and Re-Ranking Efficiency Using One Click Image Search

Jalindar

Yadav

Kshirsagar

2020

Advances in Parallel Computing

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A Novel data fusion technique to support text-based and content-based image retrieval combining different heterogeneous features is proposed. The user need to give just a single click on an query image and images recovered by content based search are re-positioned dependent on their visual and texture similitudes to the query image.Textual and visual expansions are integrated to capture user intention without additional human feedback. Expanded keywords helps in extending positive model images and furthermore develop the image pool to include more relevant images. A lot of visual features which are both efficient and effective for image search are chosen. The n-dimensional feature vector for both colour and texture is reduced to single dimension each, used for comparing the similarity with query image using suitable distance metrics. Further only the images retrieved as a result of text based search and image re-ranking process are compared during run time for finding the similar images; not the entire database. This considerably reduces the computational complexity and improves the search efficiency. With improved feature extraction capturing textual and visual similarities, the proposed one click image search framework gives a productive robotized recovery of comparable images giving promising results with improvement in retrieval efficiency.

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Section: Feature Extractionmentioning

confidence: 99%