SalNet: Deep Convolutional Networks for Saliency Prediction

No algorithm description given

1. Introduction The salience (also called saliency) of an item – be it an object, a person, a pixel, etc. – is the state or quality by which it stands out relative to its neighbors. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival by enabling organisms to focus their limited perceptual and cognitive resources on the most pertinent subset of the available sensory data. This deep learning algorithm automatically detects salients for you in a given image. Input: (Required) Image Data API Url, Web (http/https) Url, binary image or a base64 encoded image. (Optional)  Output image location. (key="location") (Optional) Saliency matrix save location. (key="saliencyLocation") Output: Image output location. Saliency matrix output location. Note: The first call to this algorithm will take a bit longer than sequential calls to due algorithm initialization. All following calls will be significantly faster. Note: The saliency matrix is a pixel-by-pixel salience representation of the image. Each pixel can have a value between 0-255, in which higher numbers correspond to higher salience. The matrix is saved as a list of lists, as in each child list represents rows of pixels in the image. Saliency Matrix Example: [
 [0, 0, 0, ..., 167, 177, 220],
 [0, 0, 0, ..., 156, 177, 210],
 .
 .
 .
 [111, 107, 101, ..., 0, 0, 0],
 [102, 129, 105, ..., 0, 0, 0],
] 2. Examples Example 1. Parameter 1: Data API Url {
 "image": "data://deeplearning/example_data/mona_lisa.jpg"
} Output {
 "output": "data://.algo/temp/mona_lisa.png"
} Example 2. Parameter 1: HTTPS Url {
 "image": "https://s3.amazonaws.com/algorithmia-assets/algo_desc_images/deeplearning_SalNet/mona_lisa.jpg"
} Output: {
 "output": "data://.algo/temp/mona_lisa.png"
} Example 3. Parameter 1: Base64 image {
 "image": "data:image/png;base64....",
} Output; {
 "output": "data://.algo/temp/output.png"
} Example 4. Parameter 1: HTTPS Url Parameter 2: Output file save location. {
 "image": "data://deeplearning/example_data/mona_lisa.jpg",
 "location": "data://.algo/temp/test42.png"
} Output: {
 "output": "data://.algo/temp/test42.png"
} 3. Credits For more information, please refer to https://github.com/imatge-upc/saliency-2016-cvpr  or  Junting Pan, Kevin McGuinness, Elisa Sayrol, Noel O'Connor, Xavier Giro-i-Nieto (2016). Shallow and Deep Convolutional Networks for Saliency Prediction. arXiv preprint arXiv:1603.00845v1. Demo image were retrieved from Wikipedia under Public https://en.wikipedia.org/wiki/File:Mona_Lisa_(copy,_Hermitage).jpg

Tags
(no tags)

Cost Breakdown

12 cr
royalty per call
1 cr
usage per second
avg duration
This algorithm has permission to call other algorithms which may incur separate royalty and usage costs.

Cost Calculator

API call duration (sec)
×
API calls
=
Estimated cost
per calls
for large volume discounts
For additional details on how pricing works, see Algorithmia pricing.

Internet access

This algorithm has Internet access. This is necessary for algorithms that rely on external services, however it also implies that this algorithm is able to send your input data outside of the Algorithmia platform.


Calls other algorithms

This algorithm has permission to call other algorithms. This allows an algorithm to compose sophisticated functionality using other algorithms as building blocks, however it also carries the potential of incurring additional royalty and usage costs from any algorithm that it calls.


To understand more about how algorithm permissions work, see the permissions documentation.

1. Type your input

2. See the result

Running algorithm...

3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/deeplearning/SalNet/0.2.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://deeplearning/SalNet/0.2.0 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia "github.com/algorithmiaio/algorithmia-go"
)

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://deeplearning/SalNet/0.2.0")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
fmt.Println(response.Result)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://deeplearning/SalNet/0.2.0");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://deeplearning/SalNet/0.2.0")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/SalNet/0.2.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://deeplearning/SalNet/0.2.0");
var response = algorithm.pipe<object>(input);
Console.WriteLine(response.result);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/SalNet/0.2.0")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/SalNet/0.2.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/SalNet/0.2.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/SalNet/0.2.0')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/SalNet/0.2.0");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "deeplearning/SalNet/0.2.0") { resp, error in
  print(resp)
}
View Swift Docs
Discussion
  • {{comment.username}}