Textual Query Based Image Retrieval

As digital cameras becoming popular and mobile phones are increased very fast so that consumers photos are increased. So that retrieving the appropriate image depending on content or text based image retrieval techniques has become very vast. Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users interests, has been an active and fast advancing research area semantic gap between the low-level visual features and the high-level semantic concepts. Real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions. Then user provides a textual query. Our system generates the inverted file to automatically find the positive Web images that are related to the textual query as well as the negative Web images that are irrelevant to the textual query. For that purpose we use k-Nearest Neighbor (kNN), Decision stumps, and linear SVM, to rank personal photos. For improvement of the photo retrieval performance, we have used two relevance feedback methods via cross-domain learning, which effectively utilize both the Web images and personal images. DOI: 10.17762/ijritcc2321-8169.15032