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BEGIN:VEVENT
UID:89c6252569869a73874f02d3805b755a6b854d9b@swoogo.com
DTSTAMP:20260817T081945Z
DESCRIPTION:Vector-based image search technology\, which encodes images and
  queries as vectors in a high-dimensional space\, is increasingly used for
  efficient image retrieval. This approach involves several key steps: extr
 acting embeddings through convolutional neural networks\, developing an in
 dexing workflow that includes preprocessing\, extracting\, and compressing
  these embeddings\, and inserting them into a searchable index. The search
  engine utilizes approximate nearest neighbor search (ANNS) and compressio
 n methods to enhance efficiency. Traditionally\, search engine optimizatio
 n for images relies on keywords either embedded in metadata or placed on h
 osting pages. However\, vector-based searches face challenges in adaptivel
 y enhancing image discoverability due to the static nature of image vector
 s. This paper introduces a novel method to optimize image discoverability 
 for vector-based search engines while minimizing visual impact. The approa
 ch frames the problem as an optimization task\, using an iterative process
  to adjust images based on intended and unwanted search queries. It employ
 s backward propagation with loss functions to fine-tune images\, generatin
 g embeddings that align with target queries and minimizing visual deviatio
 ns through perceptual loss. Segmentation masks can further direct visual a
 djustments to specific areas of an image. This technique can be applied to
  newly uploaded images or existing asset libraries to improve search ranki
 ng.
DTSTART:20241024T000000Z
DTEND:20241024T003000Z
LAST-MODIFIED:20260817T081945Z
LOCATION:Session Room 2
SEQUENCE:0
STATUS:CONFIRMED
SUMMARY:Search Engine Optimization for Vector-based Image Search
TRANSP:OPAQUE
X-ALT-DESC;FMTTYPE=text/html:<p>Vector-based image search technology\, whic
 h encodes images and queries as vectors in a high-dimensional space\, is i
 ncreasingly used for efficient image retrieval. This approach involves sev
 eral key steps: extracting embeddings through convolutional neural network
 s\, developing an indexing workflow that includes preprocessing\, extracti
 ng\, and compressing these embeddings\, and inserting them into a searchab
 le index. The search engine utilizes approximate nearest neighbor search (
 ANNS) and compression methods to enhance efficiency. Traditionally\, searc
 h engine optimization for images relies on keywords either embedded in met
 adata or placed on hosting pages. However\, vector-based searches face cha
 llenges in adaptively enhancing image discoverability due to the static na
 ture of image vectors. This paper introduces a novel method to optimize im
 age discoverability for vector-based search engines while minimizing visua
 l impact. The approach frames the problem as an optimization task\, using 
 an iterative process to adjust images based on intended and unwanted searc
 h queries. It employs backward propagation with loss functions to fine-tun
 e images\, generating embeddings that align with target queries and minimi
 zing visual deviations through perceptual loss. Segmentation masks can fur
 ther direct visual adjustments to specific areas of an image. This techniq
 ue can be applied to newly uploaded images or existing asset libraries to 
 improve search ranking.</p>
BEGIN:VALARM
UID:62326631-3065-4230-b933-633062373962
ACTION:DISPLAY
DESCRIPTION:Vector-based image search technology\, which encodes images and
  queries as vectors in a high-dimensional space\, is increasingly used for
  efficient image retrieval. This approach involves several key steps: extr
 acting embeddings through convolutional neural networks\, developing an in
 dexing workflow that includes preprocessing\, extracting\, and compressing
  these embeddings\, and inserting them into a searchable index. The search
  engine utilizes approximate nearest neighbor search (ANNS) and compressio
 n methods to enhance efficiency. Traditionally\, search engine optimizatio
 n for images relies on keywords either embedded in metadata or placed on h
 osting pages. However\, vector-based searches face challenges in adaptivel
 y enhancing image discoverability due to the static nature of image vector
 s. This paper introduces a novel method to optimize image discoverability 
 for vector-based search engines while minimizing visual impact. The approa
 ch frames the problem as an optimization task\, using an iterative process
  to adjust images based on intended and unwanted search queries. It employ
 s backward propagation with loss functions to fine-tune images\, generatin
 g embeddings that align with target queries and minimizing visual deviatio
 ns through perceptual loss. Segmentation masks can further direct visual a
 djustments to specific areas of an image. This technique can be applied to
  newly uploaded images or existing asset libraries to improve search ranki
 ng.
TRIGGER:-PT15M
END:VALARM
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